{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/AI4Finance-Foundation/FinRL-Tutorials/blob/master/5-Others/tutorial_multistock_docker.ipynb\">\n",
    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
    "</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gXaoZs2lh1hi"
   },
   "source": [
    "# Deep Reinforcement Learning for Stock Trading from Scratch: Multiple Stock Trading\n",
    "\n",
    "Tutorials to use OpenAI DRL to trade multiple stocks in one Jupyter Notebook | Presented at NeurIPS 2020: Deep RL Workshop\n",
    "\n",
    "* This blog is based on our paper: FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance, presented at NeurIPS 2020: Deep RL Workshop.\n",
    "* Check out medium blog for detailed explanations: https://towardsdatascience.com/finrl-for-quantitative-finance-tutorial-for-multiple-stock-trading-7b00763b7530\n",
    "* Please report any issues to our Github: https://github.com/AI4Finance-LLC/FinRL-Library/issues\n",
    "* **Pytorch Version** \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lGunVt8oLCVS"
   },
   "source": [
    "# Content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HOzAKQ-SLGX6"
   },
   "source": [
    "* [1. Problem Definition](#0)\n",
    "* [2. Getting Started - Load Python packages](#1)\n",
    "    * [2.1. Install Packages](#1.1)    \n",
    "    * [2.2. Check Additional Packages](#1.2)\n",
    "    * [2.3. Import Packages](#1.3)\n",
    "    * [2.4. Create Folders](#1.4)\n",
    "* [3. Download Data](#2)\n",
    "* [4. Preprocess Data](#3)        \n",
    "    * [4.1. Technical Indicators](#3.1)\n",
    "    * [4.2. Perform Feature Engineering](#3.2)\n",
    "* [5.Build Environment](#4)  \n",
    "    * [5.1. Training & Trade Data Split](#4.1)\n",
    "    * [5.2. User-defined Environment](#4.2)   \n",
    "    * [5.3. Initialize Environment](#4.3)    \n",
    "* [6.Implement DRL Algorithms](#5)  \n",
    "* [7.Backtesting Performance](#6)  \n",
    "    * [7.1. BackTestStats](#6.1)\n",
    "    * [7.2. BackTestPlot](#6.2)   \n",
    "    * [7.3. Baseline Stats](#6.3)   \n",
    "    * [7.3. Compare to Stock Market Index](#6.4)             "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sApkDlD9LIZv"
   },
   "source": [
    "<a id='0'></a>\n",
    "# Part 1. Problem Definition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HjLD2TZSLKZ-"
   },
   "source": [
    "This problem is to design an automated trading solution for single stock trading. We model the stock trading process as a Markov Decision Process (MDP). We then formulate our trading goal as a maximization problem.\n",
    "\n",
    "The algorithm is trained using Deep Reinforcement Learning (DRL) algorithms and the components of the reinforcement learning environment are:\n",
    "\n",
    "\n",
    "* Action: The action space describes the allowed actions that the agent interacts with the\n",
    "environment. Normally, a ∈ A includes three actions: a ∈ {−1, 0, 1}, where −1, 0, 1 represent\n",
    "selling, holding, and buying one stock. Also, an action can be carried upon multiple shares. We use\n",
    "an action space {−k, ..., −1, 0, 1, ..., k}, where k denotes the number of shares. For example, \"Buy\n",
    "10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or −10, respectively\n",
    "\n",
    "* Reward function: r(s, a, s′) is the incentive mechanism for an agent to learn a better action. The change of the portfolio value when action a is taken at state s and arriving at new state s',  i.e., r(s, a, s′) = v′ − v, where v′ and v represent the portfolio\n",
    "values at state s′ and s, respectively\n",
    "\n",
    "* State: The state space describes the observations that the agent receives from the environment. Just as a human trader needs to analyze various information before executing a trade, so\n",
    "our trading agent observes many different features to better learn in an interactive environment.\n",
    "\n",
    "* Environment: Dow 30 consituents\n",
    "\n",
    "\n",
    "The data of the single stock that we will be using for this case study is obtained from Yahoo Finance API. The data contains Open-High-Low-Close price and volume.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ffsre789LY08"
   },
   "source": [
    "<a id='1'></a>\n",
    "# Part 2. Getting Started- ASSUMES USING DOCKER, see readme for instructions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Uy5_PTmOh1hj"
   },
   "source": [
    "<a id='1.1'></a>\n",
    "## 2.1. Add FinRL to your path. You can of course install it as a pipy package, but this is for development purposes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "mPT0ipYE28wL"
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "sys.path.append(\"..\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "r_GgR1Cq4kr-",
    "outputId": "c9b7e8fa-d556-4a72-9f23-5df07cf76305"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.1.5\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "print(pd.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "osBHhVysOEzi"
   },
   "source": [
    "\n",
    "<a id='1.2'></a>\n",
    "## 2.2. Check if the additional packages needed are present, if not install them. \n",
    "* Yahoo Finance API\n",
    "* pandas\n",
    "* numpy\n",
    "* matplotlib\n",
    "* stockstats\n",
    "* OpenAI gym\n",
    "* stable-baselines\n",
    "* tensorflow\n",
    "* pyfolio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nGv01K8Sh1hn"
   },
   "source": [
    "<a id='1.3'></a>\n",
    "## 2.3. Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "lPqeTTwoh1hn"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "matplotlib.use('Agg')\n",
    "%matplotlib inline\n",
    "import datetime\n",
    "\n",
    "%matplotlib inline\n",
    "from finrl import config\n",
    "from finrl import config_tickers\n",
    "from finrl.meta.preprocessor.yahoodownloader import YahooDownloader\n",
    "from finrl.meta.preprocessor.preprocessors import FeatureEngineer, data_split\n",
    "from finrl.meta.env_stock_trading.env_stocktrading import StockTradingEnv\n",
    "from finrl.agents.stablebaselines3.models import DRLAgent\n",
    "from finrl.plot import backtest_stats, backtest_plot, get_daily_return, get_baseline\n",
    "\n",
    "from pprint import pprint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T2owTj985RW4"
   },
   "source": [
    "<a id='1.4'></a>\n",
    "## 2.4. Create Folders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "w9A8CN5R5PuZ"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "if not os.path.exists(\"./\" + config.DATA_SAVE_DIR):\n",
    "    os.makedirs(\"./\" + config.DATA_SAVE_DIR)\n",
    "if not os.path.exists(\"./\" + config.TRAINED_MODEL_DIR):\n",
    "    os.makedirs(\"./\" + config.TRAINED_MODEL_DIR)\n",
    "if not os.path.exists(\"./\" + config.TENSORBOARD_LOG_DIR):\n",
    "    os.makedirs(\"./\" + config.TENSORBOARD_LOG_DIR)\n",
    "if not os.path.exists(\"./\" + config.RESULTS_DIR):\n",
    "    os.makedirs(\"./\" + config.RESULTS_DIR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A289rQWMh1hq"
   },
   "source": [
    "<a id='2'></a>\n",
    "# Part 3. Download Data\n",
    "Yahoo Finance is a website that provides stock data, financial news, financial reports, etc. All the data provided by Yahoo Finance is free.\n",
    "* FinRL uses a class **YahooDownloader** to fetch data from Yahoo Finance API\n",
    "* Call Limit: Using the Public API (without authentication), you are limited to 2,000 requests per hour per IP (or up to a total of 48,000 requests a day).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NPeQ7iS-LoMm"
   },
   "source": [
    "\n",
    "\n",
    "-----\n",
    "class YahooDownloader:\n",
    "    Provides methods for retrieving daily stock data from\n",
    "    Yahoo Finance API\n",
    "\n",
    "    Attributes\n",
    "    ----------\n",
    "        start_date : str\n",
    "            start date of the data (modified from config.py)\n",
    "        end_date : str\n",
    "            end date of the data (modified from config.py)\n",
    "        ticker_list : list\n",
    "            a list of stock tickers (modified from config.py)\n",
    "\n",
    "    Methods\n",
    "    -------\n",
    "    fetch_data()\n",
    "        Fetches data from yahoo API\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "h3XJnvrbLp-C",
    "outputId": "87dea23f-469d-4e9d-de91-0f8a74929de2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2009-01-01'"
      ]
     },
     "execution_count": 35,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from config.py start_date is a string\n",
    "config.START_DATE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "FUnY8WEfLq3C",
    "outputId": "c635ae69-a13e-408f-d932-9d386d1d6dcf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2020-12-01'"
      ]
     },
     "execution_count": 36,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from config.py end_date is a string\n",
    "config.END_DATE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JzqRRTOX6aFu",
    "outputId": "d3baf63f-948a-49f9-f6f2-b7241971b8ea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['AAPL', 'MSFT', 'JPM', 'V', 'RTX', 'PG', 'GS', 'NKE', 'DIS', 'AXP', 'HD', 'INTC', 'WMT', 'IBM', 'MRK', 'UNH', 'KO', 'CAT', 'TRV', 'JNJ', 'CVX', 'MCD', 'VZ', 'CSCO', 'XOM', 'BA', 'MMM', 'PFE', 'WBA', 'DD']\n"
     ]
    }
   ],
   "source": [
    "print(config_tickers.DOW_30_TICKER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yCKm4om-s9kE",
    "outputId": "932583d8-f98b-4243-c02d-375f7272db1a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (90630, 8)\n"
     ]
    }
   ],
   "source": [
    "df = YahooDownloader(start_date = '2009-01-01',\n",
    "                     end_date = '2021-01-01',\n",
    "                     ticker_list = config_tickers.DOW_30_TICKER).fetch_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CV3HrZHLh1hy",
    "outputId": "b7b78172-8c8a-41c9-c8a6-0167edb9bd11"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90630, 8)"
      ]
     },
     "execution_count": 39,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "id": "4hYkeaPiICHS",
    "outputId": "ce9d7463-a74c-4917-c96d-848a1e8ad493"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>tic</th>\n",
       "      <th>day</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>3.067143</td>\n",
       "      <td>3.251429</td>\n",
       "      <td>3.041429</td>\n",
       "      <td>2.795913</td>\n",
       "      <td>746015200.0</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>18.570000</td>\n",
       "      <td>19.520000</td>\n",
       "      <td>18.400000</td>\n",
       "      <td>15.745411</td>\n",
       "      <td>10955700.0</td>\n",
       "      <td>AXP</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>42.799999</td>\n",
       "      <td>45.560001</td>\n",
       "      <td>42.779999</td>\n",
       "      <td>33.680935</td>\n",
       "      <td>7010200.0</td>\n",
       "      <td>BA</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>44.910000</td>\n",
       "      <td>46.980000</td>\n",
       "      <td>44.709999</td>\n",
       "      <td>32.514400</td>\n",
       "      <td>7117200.0</td>\n",
       "      <td>CAT</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>16.410000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>16.250000</td>\n",
       "      <td>12.683227</td>\n",
       "      <td>40980600.0</td>\n",
       "      <td>CSCO</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date       open       high        low      close       volume   tic  \\\n",
       "0  2009-01-02   3.067143   3.251429   3.041429   2.795913  746015200.0  AAPL   \n",
       "1  2009-01-02  18.570000  19.520000  18.400000  15.745411   10955700.0   AXP   \n",
       "2  2009-01-02  42.799999  45.560001  42.779999  33.680935    7010200.0    BA   \n",
       "3  2009-01-02  44.910000  46.980000  44.709999  32.514400    7117200.0   CAT   \n",
       "4  2009-01-02  16.410000  17.000000  16.250000  12.683227   40980600.0  CSCO   \n",
       "\n",
       "   day  \n",
       "0    4  \n",
       "1    4  \n",
       "2    4  \n",
       "3    4  \n",
       "4    4  "
      ]
     },
     "execution_count": 40,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(['date','tic'],ignore_index=True).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uqC6c40Zh1iH"
   },
   "source": [
    "# Part 4: Preprocess Data\n",
    "Data preprocessing is a crucial step for training a high quality machine learning model. We need to check for missing data and do feature engineering in order to convert the data into a model-ready state.\n",
    "* Add technical indicators. In practical trading, various information needs to be taken into account, for example the historical stock prices, current holding shares, technical indicators, etc. In this article, we demonstrate two trend-following technical indicators: MACD and RSI.\n",
    "* Add turbulence index. Risk-aversion reflects whether an investor will choose to preserve the capital. It also influences one's trading strategy when facing different market volatility level. To control the risk in a worst-case scenario, such as financial crisis of 2007–2008, FinRL employs the financial turbulence index that measures extreme asset price fluctuation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pAJKb4eL4ksB",
    "jupyter": {
     "outputs_hidden": false
    },
    "outputId": "5723ed27-b52f-4580-fd75-1d4aada92743",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully added technical indicators\n",
      "Successfully added turbulence index\n"
     ]
    }
   ],
   "source": [
    "fe = FeatureEngineer(\n",
    "                    use_technical_indicator=True,\n",
    "                    tech_indicator_list = config.INDICATORS,\n",
    "                    use_turbulence=True,\n",
    "                    user_defined_feature = False)\n",
    "\n",
    "processed = fe.preprocess_data(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "id": "grvhGJJII3Xn",
    "outputId": "91d09c37-b0e9-4c5c-d532-967e40d11f41"
   },
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>tic</th>\n",
       "      <th>day</th>\n",
       "      <th>macd</th>\n",
       "      <th>rsi_30</th>\n",
       "      <th>cci_30</th>\n",
       "      <th>dx_30</th>\n",
       "      <th>turbulence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>3.067143</td>\n",
       "      <td>3.251429</td>\n",
       "      <td>3.041429</td>\n",
       "      <td>2.795913</td>\n",
       "      <td>746015200.0</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>18.570000</td>\n",
       "      <td>19.520000</td>\n",
       "      <td>18.400000</td>\n",
       "      <td>15.745411</td>\n",
       "      <td>10955700.0</td>\n",
       "      <td>AXP</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>42.799999</td>\n",
       "      <td>45.560001</td>\n",
       "      <td>42.779999</td>\n",
       "      <td>33.680935</td>\n",
       "      <td>7010200.0</td>\n",
       "      <td>BA</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>44.910000</td>\n",
       "      <td>46.980000</td>\n",
       "      <td>44.709999</td>\n",
       "      <td>32.514400</td>\n",
       "      <td>7117200.0</td>\n",
       "      <td>CAT</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>16.410000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>16.250000</td>\n",
       "      <td>12.683227</td>\n",
       "      <td>40980600.0</td>\n",
       "      <td>CSCO</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>74.230003</td>\n",
       "      <td>77.300003</td>\n",
       "      <td>73.580002</td>\n",
       "      <td>48.043262</td>\n",
       "      <td>13695900.0</td>\n",
       "      <td>CVX</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>21.605234</td>\n",
       "      <td>22.060680</td>\n",
       "      <td>20.993229</td>\n",
       "      <td>14.527276</td>\n",
       "      <td>13251000.0</td>\n",
       "      <td>DD</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>22.760000</td>\n",
       "      <td>24.030001</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>20.597496</td>\n",
       "      <td>9796600.0</td>\n",
       "      <td>DIS</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>84.019997</td>\n",
       "      <td>87.620003</td>\n",
       "      <td>82.190002</td>\n",
       "      <td>72.844467</td>\n",
       "      <td>14088500.0</td>\n",
       "      <td>GS</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>23.070000</td>\n",
       "      <td>24.190001</td>\n",
       "      <td>22.959999</td>\n",
       "      <td>17.909452</td>\n",
       "      <td>14902500.0</td>\n",
       "      <td>HD</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date       open       high        low      close       volume   tic  \\\n",
       "0  2009-01-02   3.067143   3.251429   3.041429   2.795913  746015200.0  AAPL   \n",
       "1  2009-01-02  18.570000  19.520000  18.400000  15.745411   10955700.0   AXP   \n",
       "2  2009-01-02  42.799999  45.560001  42.779999  33.680935    7010200.0    BA   \n",
       "3  2009-01-02  44.910000  46.980000  44.709999  32.514400    7117200.0   CAT   \n",
       "4  2009-01-02  16.410000  17.000000  16.250000  12.683227   40980600.0  CSCO   \n",
       "5  2009-01-02  74.230003  77.300003  73.580002  48.043262   13695900.0   CVX   \n",
       "6  2009-01-02  21.605234  22.060680  20.993229  14.527276   13251000.0    DD   \n",
       "7  2009-01-02  22.760000  24.030001  22.500000  20.597496    9796600.0   DIS   \n",
       "8  2009-01-02  84.019997  87.620003  82.190002  72.844467   14088500.0    GS   \n",
       "9  2009-01-02  23.070000  24.190001  22.959999  17.909452   14902500.0    HD   \n",
       "\n",
       "   day  macd  rsi_30     cci_30  dx_30  turbulence  \n",
       "0    4   0.0   100.0  66.666667  100.0         0.0  \n",
       "1    4   0.0   100.0  66.666667  100.0         0.0  \n",
       "2    4   0.0   100.0  66.666667  100.0         0.0  \n",
       "3    4   0.0   100.0  66.666667  100.0         0.0  \n",
       "4    4   0.0   100.0  66.666667  100.0         0.0  \n",
       "5    4   0.0   100.0  66.666667  100.0         0.0  \n",
       "6    4   0.0   100.0  66.666667  100.0         0.0  \n",
       "7    4   0.0   100.0  66.666667  100.0         0.0  \n",
       "8    4   0.0   100.0  66.666667  100.0         0.0  \n",
       "9    4   0.0   100.0  66.666667  100.0         0.0  "
      ]
     },
     "execution_count": 50,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed.sort_values(['date','tic'],ignore_index=True).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-QsYaY0Dh1iw"
   },
   "source": [
    "<a id='4'></a>\n",
    "# Part 5. Design Environment\n",
    "Considering the stochastic and interactive nature of the automated stock trading tasks, a financial task is modeled as a **Markov Decision Process (MDP)** problem. The training process involves observing stock price change, taking an action and reward's calculation to have the agent adjusting its strategy accordingly. By interacting with the environment, the trading agent will derive a trading strategy with the maximized rewards as time proceeds.\n",
    "\n",
    "Our trading environments, based on OpenAI Gym framework, simulate live stock markets with real market data according to the principle of time-driven simulation.\n",
    "\n",
    "The action space describes the allowed actions that the agent interacts with the environment. Normally, action a includes three actions: {-1, 0, 1}, where -1, 0, 1 represent selling, holding, and buying one share. Also, an action can be carried upon multiple shares. We use an action space {-k,…,-1, 0, 1, …, k}, where k denotes the number of shares to buy and -k denotes the number of shares to sell. For example, \"Buy 10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or -10, respectively. The continuous action space needs to be normalized to [-1, 1], since the policy is defined on a Gaussian distribution, which needs to be normalized and symmetric."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5TOhcryx44bb"
   },
   "source": [
    "## Training data split: 2009-01-01 to 2018-12-31\n",
    "## Trade data split: 2019-01-01 to 2020-09-30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "W0qaVGjLtgbI",
    "outputId": "c98aeb90-84e3-4b83-9671-d679f3fe148f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75480\n",
      "15150\n"
     ]
    }
   ],
   "source": [
    "train = data_split(processed, '2009-01-01','2019-01-01')\n",
    "trade = data_split(processed, '2019-01-01','2021-01-01')\n",
    "print(len(train))\n",
    "print(len(trade))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "id": "p52zNCOhTtLR",
    "outputId": "c41f9be0-a99f-4108-a427-3112b6bd4129"
   },
   "outputs": [
    {
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       "      <td>2009-01-02</td>\n",
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       "      <td>2009-01-02</td>\n",
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       "      <td>19.520000</td>\n",
       "      <td>18.400000</td>\n",
       "      <td>15.745411</td>\n",
       "      <td>10955700.0</td>\n",
       "      <td>AXP</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
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       "      <td>42.799999</td>\n",
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       "      <td>42.779999</td>\n",
       "      <td>33.680935</td>\n",
       "      <td>7010200.0</td>\n",
       "      <td>BA</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>0</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>44.910000</td>\n",
       "      <td>46.980000</td>\n",
       "      <td>44.709999</td>\n",
       "      <td>32.514400</td>\n",
       "      <td>7117200.0</td>\n",
       "      <td>CAT</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>0</th>\n",
       "      <td>2009-01-02</td>\n",
       "      <td>16.410000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>16.250000</td>\n",
       "      <td>12.683227</td>\n",
       "      <td>40980600.0</td>\n",
       "      <td>CSCO</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "         date       open       high        low      close       volume   tic  \\\n",
       "0  2009-01-02   3.067143   3.251429   3.041429   2.795913  746015200.0  AAPL   \n",
       "0  2009-01-02  18.570000  19.520000  18.400000  15.745411   10955700.0   AXP   \n",
       "0  2009-01-02  42.799999  45.560001  42.779999  33.680935    7010200.0    BA   \n",
       "0  2009-01-02  44.910000  46.980000  44.709999  32.514400    7117200.0   CAT   \n",
       "0  2009-01-02  16.410000  17.000000  16.250000  12.683227   40980600.0  CSCO   \n",
       "\n",
       "   day  macd  rsi_30     cci_30  dx_30  turbulence  \n",
       "0    4   0.0   100.0  66.666667  100.0         0.0  \n",
       "0    4   0.0   100.0  66.666667  100.0         0.0  \n",
       "0    4   0.0   100.0  66.666667  100.0         0.0  \n",
       "0    4   0.0   100.0  66.666667  100.0         0.0  \n",
       "0    4   0.0   100.0  66.666667  100.0         0.0  "
      ]
     },
     "execution_count": 64,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 299
    },
    "id": "k9zU9YaTTvFq",
    "outputId": "705f46e4-0529-4ef5-d182-c2a1337397a4"
   },
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>38.722500</td>\n",
       "      <td>39.712502</td>\n",
       "      <td>38.557499</td>\n",
       "      <td>38.562561</td>\n",
       "      <td>148158800.0</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2</td>\n",
       "      <td>-2.019903</td>\n",
       "      <td>37.867349</td>\n",
       "      <td>-91.567852</td>\n",
       "      <td>42.250808</td>\n",
       "      <td>51.36772</td>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>93.910004</td>\n",
       "      <td>96.269997</td>\n",
       "      <td>93.769997</td>\n",
       "      <td>92.319603</td>\n",
       "      <td>4175400.0</td>\n",
       "      <td>AXP</td>\n",
       "      <td>2</td>\n",
       "      <td>-3.414037</td>\n",
       "      <td>41.205002</td>\n",
       "      <td>-97.751316</td>\n",
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       "      <td>51.36772</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>316.190002</td>\n",
       "      <td>323.950012</td>\n",
       "      <td>313.709991</td>\n",
       "      <td>314.645142</td>\n",
       "      <td>3292200.0</td>\n",
       "      <td>BA</td>\n",
       "      <td>2</td>\n",
       "      <td>-5.550592</td>\n",
       "      <td>47.010000</td>\n",
       "      <td>-21.712382</td>\n",
       "      <td>13.611972</td>\n",
       "      <td>51.36772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>124.029999</td>\n",
       "      <td>127.879997</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>119.302582</td>\n",
       "      <td>4783200.0</td>\n",
       "      <td>CAT</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.686759</td>\n",
       "      <td>48.229089</td>\n",
       "      <td>-5.091209</td>\n",
       "      <td>0.873482</td>\n",
       "      <td>51.36772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>42.279999</td>\n",
       "      <td>43.200001</td>\n",
       "      <td>42.209999</td>\n",
       "      <td>40.057236</td>\n",
       "      <td>23833500.0</td>\n",
       "      <td>CSCO</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.952338</td>\n",
       "      <td>44.872557</td>\n",
       "      <td>-87.518839</td>\n",
       "      <td>29.529377</td>\n",
       "      <td>51.36772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date        open        high         low       close       volume  \\\n",
       "0  2019-01-02   38.722500   39.712502   38.557499   38.562561  148158800.0   \n",
       "0  2019-01-02   93.910004   96.269997   93.769997   92.319603    4175400.0   \n",
       "0  2019-01-02  316.190002  323.950012  313.709991  314.645142    3292200.0   \n",
       "0  2019-01-02  124.029999  127.879997  123.000000  119.302582    4783200.0   \n",
       "0  2019-01-02   42.279999   43.200001   42.209999   40.057236   23833500.0   \n",
       "\n",
       "    tic  day      macd     rsi_30     cci_30      dx_30  turbulence  \n",
       "0  AAPL    2 -2.019903  37.867349 -91.567852  42.250808    51.36772  \n",
       "0   AXP    2 -3.414037  41.205002 -97.751316  26.709417    51.36772  \n",
       "0    BA    2 -5.550592  47.010000 -21.712382  13.611972    51.36772  \n",
       "0   CAT    2 -0.686759  48.229089  -5.091209   0.873482    51.36772  \n",
       "0  CSCO    2 -0.952338  44.872557 -87.518839  29.529377    51.36772  "
      ]
     },
     "execution_count": 65,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trade.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zYN573SOHhxG",
    "outputId": "187c6d1b-3e91-40f8-dafd-230d787f2ee1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['macd', 'rsi_30', 'cci_30', 'dx_30']"
      ]
     },
     "execution_count": 66,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config.INDICATORS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ko9DO0mW4ksE"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from gym.utils import seeding\n",
    "import gym\n",
    "from gym import spaces\n",
    "import matplotlib\n",
    "from copy import deepcopy\n",
    "\n",
    "matplotlib.use(\"Agg\")\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "from stable_baselines3.common.vec_env import DummyVecEnv\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from gym.utils import seeding\n",
    "import gym\n",
    "from gym import spaces\n",
    "import matplotlib\n",
    "\n",
    "matplotlib.use(\"Agg\")\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "from stable_baselines3.common.vec_env import DummyVecEnv, SubprocVecEnv\n",
    "from stable_baselines3.common import logger\n",
    "\n",
    "class StockTradingEnvV2(gym.Env):\n",
    "    \n",
    "    \"\"\"\n",
    "    A stock trading environment for OpenAI gym\n",
    "    Parameters:\n",
    "    state space: {start_cash, <owned_shares>, for s in stocks{<stock.values>}, }\n",
    "        df (pandas.DataFrame): Dataframe containing data\n",
    "        transaction_cost (float): cost for buying or selling shares\n",
    "        hmax (int): max number of share purchases allowed per asset\n",
    "        turbulence_threshold (float): Maximum turbulence allowed in market for purchases to occur. If exceeded, positions are liquidated\n",
    "        print_verbosity(int): When iterating (step), how often to print stats about state of env\n",
    "        reward_scaling (float): Scaling value to multiply reward by at each step. \n",
    "        initial_amount: (int, float): Amount of cash initially available\n",
    "        daily_information_columns (list(str)): Columns to use when building state space from the dataframe. \n",
    "        out_of_cash_penalty (int, float): Penalty to apply if the algorithm runs out of cash\n",
    "    \n",
    "\n",
    "\n",
    "    tests:\n",
    "        after reset, static strategy should result in same metrics\n",
    "\n",
    "        buy zero should result in no costs, no assets purchased\n",
    "        given no change in prices, no change in asset values\n",
    "    \"\"\"\n",
    "    metadata = {\"render.modes\": [\"human\"]}\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        df,\n",
    "        transaction_cost_pct=3e-3,\n",
    "        date_col_name=\"date\",\n",
    "        hmax=10,\n",
    "        turbulence_threshold=None,\n",
    "        print_verbosity=10,\n",
    "        reward_scaling=1e-4,\n",
    "        initial_amount=1e6,\n",
    "        daily_information_cols=[\"open\", \"close\", \"high\", \"low\", \"volume\"],\n",
    "        out_of_cash_penalty=None,\n",
    "        cache_indicator_data = True\n",
    "    ):\n",
    "        self.df = df\n",
    "        self.stock_col = \"tic\"\n",
    "        self.assets = df[self.stock_col].unique()\n",
    "        self.dates = df[date_col_name].sort_values().unique()\n",
    "        self.df = self.df.set_index(date_col_name)\n",
    "        self.hmax = hmax\n",
    "        self.initial_amount = initial_amount\n",
    "        if out_of_cash_penalty is None:\n",
    "            out_of_cash_penalty=-initial_amount*0.5\n",
    "        self.out_of_cash_penalty = out_of_cash_penalty\n",
    "        self.print_verbosity = print_verbosity\n",
    "        self.transaction_cost_pct = transaction_cost_pct\n",
    "        self.reward_scaling = reward_scaling\n",
    "        self.daily_information_cols = daily_information_cols\n",
    "        self.close_index = self.daily_information_cols.index(\"close\")\n",
    "        self.state_space = (\n",
    "            1 + len(self.assets) + len(self.assets) * len(self.daily_information_cols)\n",
    "        )\n",
    "        self.action_space = spaces.Box(low=-1, high=1, shape=(len(self.assets),))\n",
    "        self.observation_space = spaces.Box(\n",
    "            low=-np.inf, high=np.inf, shape=(self.state_space,)\n",
    "        )\n",
    "        self.episode = -1  # initialize so we can call reset\n",
    "        self.seed()\n",
    "        self.episode_history = []\n",
    "        self.printed_header = False\n",
    "        self.cache_indicator_data = cache_indicator_data\n",
    "        self.cached_data = None\n",
    "        if self.cache_indicator_data:\n",
    "            print(\"caching data\")\n",
    "            self.cached_data = [self.get_date_vector(i) for i, _ in enumerate(self.dates)]\n",
    "            print(\"data cached!\")\n",
    "        \n",
    "\n",
    "    def seed(self):\n",
    "        pass\n",
    "\n",
    "\n",
    "    def reset(self):\n",
    "        self.sum_trades = 0\n",
    "        self.date_index = 0\n",
    "        self.episode += 1\n",
    "        self.actions_memory = []\n",
    "        self.state_memory = []\n",
    "        self.account_information = {\n",
    "            \"cash\": [],\n",
    "            \"asset_value\": [],\n",
    "            \"total_assets\": [],\n",
    "            'reward': []\n",
    "        }\n",
    "        self.state_memory.append(\n",
    "            np.array(\n",
    "                [self.initial_amount]\n",
    "                + [0] * len(self.assets)\n",
    "                + self.get_date_vector(self.date_index)\n",
    "            )\n",
    "        )\n",
    "        return [0 for _ in range(self.state_space)]\n",
    "\n",
    "    def get_date_vector(self, date, cols=None):\n",
    "        if (cols is None) and (self.cached_data is not None):\n",
    "            return self.cached_data[date]\n",
    "        else:\n",
    "            date = self.dates[date]\n",
    "            if cols is None:\n",
    "                cols = self.daily_information_cols\n",
    "            trunc_df = self.df.loc[date]\n",
    "            v = []\n",
    "            for a in self.assets:\n",
    "                subset = trunc_df[trunc_df[self.stock_col] == a]\n",
    "                v += subset.loc[date, cols].tolist()\n",
    "            assert len(v) == len(self.assets) * len(cols)\n",
    "            return v\n",
    "    \n",
    "    def log_step(self, reason, terminal_reward=None):\n",
    "        if terminal_reward is None:\n",
    "            terminal_reward = self.account_information['reward'][-1]\n",
    "        cash_pct = self.account_information['cash'][-1]/self.account_information['total_assets'][-1]\n",
    "        rec = [self.episode, self.date_index, reason, f\"${int(self.account_information['total_assets'][-1])}\",f\"${terminal_reward:0.2f}\", f\"{cash_pct*100:0.2f}%\"]\n",
    "\n",
    "        self.episode_history.append(rec)\n",
    "        print(self.template.format(*rec))\n",
    "\n",
    "    def step(self, actions):\n",
    "        #print header only first time\n",
    "        if self.printed_header is False:\n",
    "            self.template = \"{0:8}|{1:10}|{2:15}|{3:7}|{4:10}|{5:10}\" # column widths: 8, 10, 15, 7, 10\n",
    "            print(self.template.format(\"EPISODE\", \"STEPS\", \"TERMINAL_REASON\", \"TOT_ASSETS\", \"TERMINAL_REWARD_unsc\", \"CASH_PCT\"))\n",
    "            self.printed_header = True\n",
    "\n",
    "        # define terminal function in scope so we can do something about the cycle being over\n",
    "        def return_terminal(reason='Last Date', extra_reward=0):\n",
    "\n",
    "            state = self.state_memory[-1]\n",
    "            reward = 0\n",
    "            reward += extra_reward\n",
    "            self.log_step(reason = reason, terminal_reward= reward)\n",
    "            reward = reward*self.reward_scaling\n",
    "            # Add outputs to logger interface\n",
    "            reward_pct = self.account_information['total_assets'][-1]/self.initial_amount\n",
    "            logger.record(\"environment/total_reward_pct\", (reward_pct-1)*100)\n",
    "            logger.record(\"environment/daily_trades\", self.sum_trades/self.date_index)\n",
    "            logger.record(\"environment/completed_steps\", self.date_index)\n",
    "            logger.record(\"environment/sum_rewards\", np.sum(self.account_information['reward']))\n",
    "            return state, reward, True, {}\n",
    "\n",
    "        # print if it's time.\n",
    "        if (self.date_index + 1) % self.print_verbosity == 0:\n",
    "            self.log_step(reason = 'update')\n",
    "\n",
    "        #if we're at the end\n",
    "        if self.date_index == len(self.dates) - 1:\n",
    "            #if we hit the end, set reward to total gains (or losses)\n",
    "            terminal_reward = self.account_information['total_assets'][-1]-self.initial_amount\n",
    "            return return_terminal(extra_reward = terminal_reward)\n",
    "        else:\n",
    "            begin_cash = self.state_memory[-1][0]\n",
    "            holdings = self.state_memory[-1][1 : len(self.assets) + 1]\n",
    "            assert (min(holdings)>=0)\n",
    "            closings = np.array(self.get_date_vector(self.date_index, cols=[\"close\"]))\n",
    "\n",
    "            # compute current value of holdings\n",
    "            asset_value = np.dot(holdings, closings)\n",
    "\n",
    "            # reward is (cash + assets) - (cash_last_step + assets_last_step)\n",
    "            if self.date_index==0:\n",
    "                reward = 0\n",
    "            else:\n",
    "                reward = (\n",
    "                    begin_cash + asset_value - self.account_information[\"total_assets\"][-1]\n",
    "                )\n",
    "\n",
    "            # log the values of cash, assets, and total assets\n",
    "            self.account_information[\"cash\"].append(begin_cash)\n",
    "            self.account_information[\"asset_value\"].append(asset_value)\n",
    "            self.account_information[\"total_assets\"].append(begin_cash + asset_value)\n",
    "            self.account_information['reward'].append(reward)\n",
    "\n",
    "            # multiply action values by our scalar multiplier and save\n",
    "            actions = actions * self.hmax\n",
    "            self.actions_memory.append(actions)\n",
    "\n",
    "            # clip actions so we can't sell more assets than we hold\n",
    "            actions = np.maximum(actions, -np.array(holdings))\n",
    "            self.sum_trades += np.sum(np.abs(actions))\n",
    "\n",
    "            # compute our proceeds from sales, and add to cash\n",
    "            sells = -np.clip(actions, -np.inf, 0)\n",
    "            proceeds = np.dot(sells, closings)\n",
    "            costs = proceeds * self.transaction_cost_pct\n",
    "            coh = begin_cash + proceeds\n",
    "\n",
    "            # compute the cost of our buys\n",
    "            buys = np.clip(actions, 0, np.inf)\n",
    "            spend = np.dot(buys, closings)\n",
    "            costs += spend * self.transaction_cost_pct\n",
    "\n",
    "            # if we run out of cash, end the cycle and penalize\n",
    "            if (spend + costs) > coh:\n",
    "                return return_terminal(reason = 'CASH SHORTAGE',\n",
    "                    extra_reward=self.out_of_cash_penalty,\n",
    "                )\n",
    "\n",
    "            # verify we didn't do anything impossible here\n",
    "            assert (spend + costs) <= coh\n",
    "\n",
    "            # update our holdings\n",
    "            coh = coh - spend - costs\n",
    "            holdings_updated = holdings + actions\n",
    "            self.date_index += 1\n",
    "            state = (\n",
    "                [coh] + list(holdings_updated) + self.get_date_vector(self.date_index)\n",
    "            )\n",
    "            self.state_memory.append(state)\n",
    "            reward = reward * self.reward_scaling\n",
    "            return state, reward, False, {}\n",
    "\n",
    "    def get_sb_env(self):\n",
    "        e = DummyVecEnv([lambda: self])\n",
    "        obs = e.reset()\n",
    "        return e, obs\n",
    "    \n",
    "    def get_multiproc_env(self, n = 10):\n",
    "        def get_self():\n",
    "            return deepcopy(self)\n",
    "        e = SubprocVecEnv([get_self for _ in range(n)], start_method = 'fork')\n",
    "        obs = e.reset()\n",
    "        return e, obs\n",
    "\n",
    "    def save_asset_memory(self):\n",
    "        self.account_information[\"date\"] = self.dates[: len(self.account_information['cash'])]\n",
    "        return pd.DataFrame(self.account_information)\n",
    "\n",
    "    def save_action_memory(self):\n",
    "        return pd.DataFrame(\n",
    "            {\"date\": self.dates[: self.date_index], \"actions\": self.actions_memory}\n",
    "        )\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Cj8Muk1w4ksE",
    "outputId": "ff67b6e0-81db-4e48-e038-cfa2ad0a80cd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    A stock trading environment for OpenAI gym\n",
      "    Parameters:\n",
      "    state space: {start_cash, <owned_shares>, for s in stocks{<stock.values>}, }\n",
      "        df (pandas.DataFrame): Dataframe containing data\n",
      "        transaction_cost (float): cost for buying or selling shares\n",
      "        hmax (int): max number of share purchases allowed per asset\n",
      "        turbulence_threshold (float): Maximum turbulence allowed in market for purchases to occur. If exceeded, positions are liquidated\n",
      "        print_verbosity(int): When iterating (step), how often to print stats about state of env\n",
      "        reward_scaling (float): Scaling value to multiply reward by at each step. \n",
      "        initial_amount: (int, float): Amount of cash initially available\n",
      "        daily_information_columns (list(str)): Columns to use when building state space from the dataframe. \n",
      "        out_of_cash_penalty (int, float): Penalty to apply if the algorithm runs out of cash\n",
      "    \n",
      "\n",
      "\n",
      "    tests:\n",
      "        after reset, static strategy should result in same metrics\n",
      "\n",
      "        buy zero should result in no costs, no assets purchased\n",
      "        given no change in prices, no change in asset values\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "print(StockTradingEnvV2.__doc__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q2zqII8rMIqn",
    "outputId": "8a2c943b-1be4-4b8d-b64f-666e0852b7e6"
   },
   "source": [
    "#### state space\n",
    "The state space of the observation is as follows \n",
    "\n",
    "`start_cash, <owned_shares_of_n_assets>, <<indicator_i_for_asset_j> for j in assets>`\n",
    "\n",
    "indicators are any daily measurement you can achieve. Common ones are 'volume', 'open' 'close' 'high', 'low'.\n",
    "However, you can add these as needed, \n",
    "The feature engineer adds indicators, and you can add your own as well. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "AWyp84Ltto19",
    "outputId": "89e6de9b-888c-49fa-cd09-df941d5f0352"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "caching data\n",
      "data cached!\n"
     ]
    }
   ],
   "source": [
    "information_cols = ['open', 'high', 'low', 'close', 'volume', 'day', 'macd', 'rsi_30', 'cci_30', 'dx_30', 'turbulence']\n",
    "\n",
    "e_train_gym = StockTradingEnvV2(df = train, \n",
    "                              hmax = 100, \n",
    "                              out_of_cash_penalty=-1e6,\n",
    "                              daily_information_cols = information_cols,\n",
    "                              print_verbosity = 500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "64EoqOrQjiVf"
   },
   "source": [
    "## Environment for Training\n",
    "There are two available environments. The multiprocessing and the single processing env. \n",
    "Some models won't work with multiprocessing. \n",
    "\n",
    "```python\n",
    "# single processing\n",
    "env_train, _ = e_train_gym.get_sb_env()\n",
    "\n",
    "\n",
    "#multiprocessing\n",
    "env_train, _ = e_train_gym.get_multiproc_env(n = <n_cores>)\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xwSvvPjutpqS",
    "outputId": "406e5ec3-28ba-4a72-9b22-0d031f7bf9a6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using 12 cores\n"
     ]
    }
   ],
   "source": [
    "# for this example, let's do multiprocessing with n_cores-2\n",
    "\n",
    "import multiprocessing\n",
    "\n",
    "n_cores = multiprocessing.cpu_count() - 2\n",
    "n_cores = 12\n",
    "print(f\"using {n_cores} cores\")\n",
    "\n",
    "\n",
    "# env_train, _ = e_train_gym.get_multiproc_env(n = n_cores)\n",
    "env_train, _ = e_train_gym.get_sb_env()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HMNR5nHjh1iz"
   },
   "source": [
    "<a id='5'></a>\n",
    "# Part 6: Implement DRL Algorithms\n",
    "* The implementation of the DRL algorithms are based on **OpenAI Baselines** and **Stable Baselines**. Stable Baselines is a fork of OpenAI Baselines, with a major structural refactoring, and code cleanups.\n",
    "* FinRL library includes fine-tuned standard DRL algorithms, such as DQN, DDPG,\n",
    "Multi-Agent DDPG, PPO, SAC, A2C and TD3. We also allow users to\n",
    "design their own DRL algorithms by adapting these DRL algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "364PsqckttcQ"
   },
   "outputs": [],
   "source": [
    "agent = DRLAgent(env = env_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "zcmsP8B04ksF",
    "outputId": "faeffd89-b5c2-420c-bd7f-face2ff8bf61"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_steps': 2048, 'ent_coef': 0.01, 'learning_rate': 0.00025, 'batch_size': 64}\n"
     ]
    }
   ],
   "source": [
    "print(config.PPO_PARAMS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YDmqOyF9h1iz"
   },
   "source": [
    "### Model Training: 5 models, A2C DDPG, PPO, TD3, SAC\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uijiWgkuh1jB"
   },
   "source": [
    "### Model 1: A2C\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "GUCnkn-HIbmj"
   },
   "outputs": [],
   "source": [
    "# from torch.nn import Softsign\n",
    "# a2c_params = {\n",
    "#     \"ent_coef\": 0.01, \n",
    "#     \"learning_rate\": 9e-4,\n",
    "#     \"n_steps\": 10, \n",
    "#     \"gamma\": 0.98\n",
    "# }\n",
    "\n",
    "# policy_kwargs = {\n",
    "#     \"activation_fn\": Softsign\n",
    "# }\n",
    "\n",
    "# model = agent.get_model(\"a2c\",  model_kwargs = a2c_params, policy_kwargs = policy_kwargs)\n",
    "\n",
    "# model.load(\"quicksave_a2c_dow.model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "DuqXkggh4ksG",
    "outputId": "1d89af33-4c4a-497c-ac6a-e9f03856e816"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_steps': 128, 'ent_coef': 0.01, 'learning_rate': 0.00025, 'batch_size': 256, 'gamma': 0.99}\n"
     ]
    }
   ],
   "source": [
    "from torch.nn import Softsign, ReLU\n",
    "ppo_params ={'n_steps': 128, \n",
    "             'ent_coef': 0.01, \n",
    "             'learning_rate': 0.00025, \n",
    "             'batch_size': 256, \n",
    "            'gamma': 0.99}\n",
    "\n",
    "policy_kwargs = {\n",
    "#     \"activation_fn\": ReLU,\n",
    "    \"net_arch\": [1024, 1024, 1024], \n",
    "#     \"squash_output\": True\n",
    "}\n",
    "\n",
    "model = agent.get_model(\"ppo\",  model_kwargs = ppo_params, policy_kwargs = policy_kwargs, verbose = 0)\n",
    "# model.load(\"quicksave_ppo_dow.model\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7fRVjxB14ksG",
    "outputId": "0d9e4f27-7ef3-4508-b970-e51cacdc297b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPISODE |STEPS     |TERMINAL_REASON|TOT_ASSETS|TERMINAL_REWARD_unsc|CASH_PCT  \n",
      "       1|       184|CASH SHORTAGE  |$1126336|$-1000000.00|0.64%     \n",
      "       2|       120|CASH SHORTAGE  |$1053779|$-1000000.00|0.71%     \n",
      "       3|       101|CASH SHORTAGE  |$1075721|$-1000000.00|1.32%     \n",
      "       4|        95|CASH SHORTAGE  |$1073623|$-1000000.00|0.15%     \n",
      "       5|        88|CASH SHORTAGE  |$1064355|$-1000000.00|0.33%     \n",
      "       6|        87|CASH SHORTAGE  |$1127252|$-1000000.00|0.65%     \n",
      "       7|        91|CASH SHORTAGE  |$1088652|$-1000000.00|0.41%     \n",
      "       8|       102|CASH SHORTAGE  |$1120612|$-1000000.00|0.63%     \n",
      "       9|       104|CASH SHORTAGE  |$1143759|$-1000000.00|1.76%     \n",
      "      10|       102|CASH SHORTAGE  |$1117993|$-1000000.00|0.19%     \n",
      "      11|       115|CASH SHORTAGE  |$1078617|$-1000000.00|1.43%     \n",
      "      12|       109|CASH SHORTAGE  |$1101502|$-1000000.00|0.47%     \n",
      "      13|       119|CASH SHORTAGE  |$1015447|$-1000000.00|2.24%     \n",
      "      14|       110|CASH SHORTAGE  |$1119033|$-1000000.00|1.66%     \n",
      "      15|       112|CASH SHORTAGE  |$1125303|$-1000000.00|1.62%     \n",
      "      16|        99|CASH SHORTAGE  |$1094791|$-1000000.00|0.70%     \n",
      "      17|        89|CASH SHORTAGE  |$1101357|$-1000000.00|0.68%     \n",
      "      18|        91|CASH SHORTAGE  |$1065212|$-1000000.00|1.72%     \n",
      "      19|        93|CASH SHORTAGE  |$1065110|$-1000000.00|0.03%     \n",
      "      20|        98|CASH SHORTAGE  |$1096579|$-1000000.00|2.51%     \n",
      "      21|        98|CASH SHORTAGE  |$1071870|$-1000000.00|0.93%     \n",
      "      22|        98|CASH SHORTAGE  |$1062253|$-1000000.00|1.78%     \n",
      "      23|       108|CASH SHORTAGE  |$1067222|$-1000000.00|1.14%     \n",
      "      24|       107|CASH SHORTAGE  |$1121438|$-1000000.00|0.08%     \n",
      "      25|       119|CASH SHORTAGE  |$1039618|$-1000000.00|0.77%     \n",
      "      26|       109|CASH SHORTAGE  |$1122299|$-1000000.00|0.42%     \n",
      "      27|       108|CASH SHORTAGE  |$1148271|$-1000000.00|1.86%     \n",
      "      28|       106|CASH SHORTAGE  |$1122218|$-1000000.00|1.12%     \n",
      "      29|        90|CASH SHORTAGE  |$1058676|$-1000000.00|0.81%     \n",
      "      30|        71|CASH SHORTAGE  |$1040990|$-1000000.00|0.97%     \n",
      "      31|        85|CASH SHORTAGE  |$1100779|$-1000000.00|0.61%     \n",
      "      32|        95|CASH SHORTAGE  |$1062993|$-1000000.00|0.22%     \n",
      "      33|        95|CASH SHORTAGE  |$1081670|$-1000000.00|0.60%     \n",
      "      34|        92|CASH SHORTAGE  |$1074433|$-1000000.00|0.20%     \n",
      "      35|        87|CASH SHORTAGE  |$1138220|$-1000000.00|0.09%     \n",
      "      36|        88|CASH SHORTAGE  |$1090888|$-1000000.00|2.75%     \n",
      "      37|        85|CASH SHORTAGE  |$1104437|$-1000000.00|1.07%     \n",
      "      38|        73|CASH SHORTAGE  |$1018646|$-1000000.00|0.08%     \n",
      "      39|        77|CASH SHORTAGE  |$1047679|$-1000000.00|0.29%     \n",
      "      40|        53|CASH SHORTAGE  |$944222|$-1000000.00|0.26%     \n",
      "      41|        72|CASH SHORTAGE  |$1051275|$-1000000.00|0.32%     \n",
      "      42|        75|CASH SHORTAGE  |$1041833|$-1000000.00|0.97%     \n",
      "      43|        71|CASH SHORTAGE  |$1060736|$-1000000.00|0.34%     \n",
      "      44|        54|CASH SHORTAGE  |$1035496|$-1000000.00|0.28%     \n",
      "      45|        78|CASH SHORTAGE  |$1036512|$-1000000.00|1.20%     \n",
      "      46|        91|CASH SHORTAGE  |$1098024|$-1000000.00|0.83%     \n",
      "      47|        73|CASH SHORTAGE  |$1019493|$-1000000.00|0.77%     \n",
      "      48|        67|CASH SHORTAGE  |$1056234|$-1000000.00|0.71%     \n",
      "      49|        66|CASH SHORTAGE  |$1070277|$-1000000.00|0.43%     \n",
      "      50|        63|CASH SHORTAGE  |$1082124|$-1000000.00|0.38%     \n",
      "      51|        53|CASH SHORTAGE  |$975713|$-1000000.00|0.07%     \n",
      "      52|        49|CASH SHORTAGE  |$938692|$-1000000.00|1.24%     \n",
      "      53|        49|CASH SHORTAGE  |$947387|$-1000000.00|0.96%     \n",
      "      54|        74|CASH SHORTAGE  |$1085770|$-1000000.00|0.52%     \n",
      "      55|        72|CASH SHORTAGE  |$1065439|$-1000000.00|0.83%     \n",
      "      56|        84|CASH SHORTAGE  |$1119847|$-1000000.00|0.85%     \n",
      "      57|        90|CASH SHORTAGE  |$1088719|$-1000000.00|1.66%     \n",
      "      58|        99|CASH SHORTAGE  |$1101179|$-1000000.00|1.14%     \n",
      "      59|       109|CASH SHORTAGE  |$1131170|$-1000000.00|1.44%     \n",
      "      60|        78|CASH SHORTAGE  |$1041081|$-1000000.00|1.35%     \n",
      "      61|       121|CASH SHORTAGE  |$1082333|$-1000000.00|0.68%     \n",
      "      62|        67|CASH SHORTAGE  |$1030780|$-1000000.00|0.82%     \n",
      "      63|        74|CASH SHORTAGE  |$1025450|$-1000000.00|0.15%     \n",
      "      64|        88|CASH SHORTAGE  |$1093301|$-1000000.00|1.48%     \n",
      "      65|        92|CASH SHORTAGE  |$1069173|$-1000000.00|0.08%     \n",
      "      66|        79|CASH SHORTAGE  |$1022121|$-1000000.00|0.32%     \n",
      "      67|       130|CASH SHORTAGE  |$1058939|$-1000000.00|0.99%     \n",
      "      68|       138|CASH SHORTAGE  |$1140314|$-1000000.00|0.10%     \n",
      "      69|       126|CASH SHORTAGE  |$1045062|$-1000000.00|0.16%     \n",
      "      70|       121|CASH SHORTAGE  |$1055635|$-1000000.00|0.55%     \n",
      "      71|       117|CASH SHORTAGE  |$1043064|$-1000000.00|0.32%     \n",
      "      72|       116|CASH SHORTAGE  |$1084454|$-1000000.00|0.29%     \n",
      "      73|       107|CASH SHORTAGE  |$1132892|$-1000000.00|1.60%     \n",
      "      74|       101|CASH SHORTAGE  |$1095473|$-1000000.00|0.07%     \n",
      "      75|        96|CASH SHORTAGE  |$1073010|$-1000000.00|0.89%     \n",
      "      76|        98|CASH SHORTAGE  |$1098668|$-1000000.00|0.41%     \n",
      "      77|        84|CASH SHORTAGE  |$1072925|$-1000000.00|0.56%     \n",
      "      78|        83|CASH SHORTAGE  |$1091200|$-1000000.00|1.52%     \n",
      "      79|        84|CASH SHORTAGE  |$1068597|$-1000000.00|0.74%     \n",
      "      80|        81|CASH SHORTAGE  |$1050425|$-1000000.00|1.81%     \n",
      "      81|        72|CASH SHORTAGE  |$1031715|$-1000000.00|0.89%     \n",
      "      82|        75|CASH SHORTAGE  |$980377|$-1000000.00|0.17%     \n",
      "      83|        73|CASH SHORTAGE  |$979268|$-1000000.00|0.55%     \n",
      "      84|        69|CASH SHORTAGE  |$982560|$-1000000.00|1.18%     \n",
      "      85|        69|CASH SHORTAGE  |$981757|$-1000000.00|1.03%     \n",
      "      86|        69|CASH SHORTAGE  |$981298|$-1000000.00|0.67%     \n",
      "      87|        64|CASH SHORTAGE  |$991946|$-1000000.00|0.93%     \n",
      "      88|        62|CASH SHORTAGE  |$990354|$-1000000.00|1.25%     \n",
      "      89|        53|CASH SHORTAGE  |$908547|$-1000000.00|0.42%     \n",
      "      90|        59|CASH SHORTAGE  |$940157|$-1000000.00|1.90%     \n",
      "      91|        64|CASH SHORTAGE  |$992696|$-1000000.00|0.90%     \n",
      "      92|        52|CASH SHORTAGE  |$920937|$-1000000.00|1.33%     \n",
      "      93|        65|CASH SHORTAGE  |$978896|$-1000000.00|1.31%     \n",
      "      94|        59|CASH SHORTAGE  |$936217|$-1000000.00|2.13%     \n",
      "      95|        64|CASH SHORTAGE  |$992192|$-1000000.00|0.47%     \n",
      "      96|        60|CASH SHORTAGE  |$927495|$-1000000.00|1.79%     \n",
      "      97|        55|CASH SHORTAGE  |$937308|$-1000000.00|0.41%     \n",
      "      98|        59|CASH SHORTAGE  |$927707|$-1000000.00|1.30%     \n",
      "      99|        60|CASH SHORTAGE  |$932933|$-1000000.00|1.17%     \n",
      "     100|        62|CASH SHORTAGE  |$975696|$-1000000.00|0.56%     \n",
      "     101|        63|CASH SHORTAGE  |$986689|$-1000000.00|0.76%     \n",
      "     102|        64|CASH SHORTAGE  |$989831|$-1000000.00|1.69%     \n",
      "     103|        59|CASH SHORTAGE  |$931324|$-1000000.00|0.16%     \n",
      "     104|        53|CASH SHORTAGE  |$894562|$-1000000.00|1.48%     \n",
      "     105|        50|CASH SHORTAGE  |$920658|$-1000000.00|0.23%     \n",
      "     106|        47|CASH SHORTAGE  |$890883|$-1000000.00|1.12%     \n",
      "     107|        50|CASH SHORTAGE  |$917876|$-1000000.00|1.71%     \n",
      "     108|        49|CASH SHORTAGE  |$893738|$-1000000.00|0.03%     \n",
      "     109|        55|CASH SHORTAGE  |$940842|$-1000000.00|0.13%     \n",
      "     110|        50|CASH SHORTAGE  |$920551|$-1000000.00|1.38%     \n",
      "     111|        52|CASH SHORTAGE  |$912244|$-1000000.00|0.70%     \n",
      "     112|        55|CASH SHORTAGE  |$932466|$-1000000.00|1.13%     \n",
      "     113|        60|CASH SHORTAGE  |$925802|$-1000000.00|0.14%     \n",
      "     114|        63|CASH SHORTAGE  |$964860|$-1000000.00|2.01%     \n",
      "     115|        70|CASH SHORTAGE  |$990104|$-1000000.00|1.03%     \n",
      "     116|        69|CASH SHORTAGE  |$966294|$-1000000.00|0.42%     \n",
      "     117|        68|CASH SHORTAGE  |$989853|$-1000000.00|0.41%     \n",
      "     118|        63|CASH SHORTAGE  |$984990|$-1000000.00|0.70%     \n",
      "     119|        63|CASH SHORTAGE  |$994698|$-1000000.00|1.65%     \n",
      "     120|        61|CASH SHORTAGE  |$948300|$-1000000.00|0.99%     \n",
      "     121|        59|CASH SHORTAGE  |$929205|$-1000000.00|0.47%     \n",
      "     122|        57|CASH SHORTAGE  |$971198|$-1000000.00|0.09%     \n",
      "     123|        55|CASH SHORTAGE  |$936595|$-1000000.00|1.35%     \n",
      "     124|        55|CASH SHORTAGE  |$932407|$-1000000.00|0.97%     \n",
      "     125|        57|CASH SHORTAGE  |$969697|$-1000000.00|1.16%     \n",
      "     126|        57|CASH SHORTAGE  |$971237|$-1000000.00|1.98%     \n",
      "     127|        59|CASH SHORTAGE  |$918741|$-1000000.00|0.45%     \n",
      "     128|        55|CASH SHORTAGE  |$920377|$-1000000.00|1.23%     \n",
      "     129|        51|CASH SHORTAGE  |$922118|$-1000000.00|1.05%     \n",
      "     130|        57|CASH SHORTAGE  |$962783|$-1000000.00|0.10%     \n",
      "     131|        58|CASH SHORTAGE  |$940099|$-1000000.00|1.11%     \n",
      "     132|        58|CASH SHORTAGE  |$947875|$-1000000.00|0.36%     \n",
      "     133|        55|CASH SHORTAGE  |$923767|$-1000000.00|2.45%     \n",
      "     134|        56|CASH SHORTAGE  |$941254|$-1000000.00|0.05%     \n",
      "     135|        53|CASH SHORTAGE  |$886847|$-1000000.00|1.29%     \n",
      "     136|        55|CASH SHORTAGE  |$930745|$-1000000.00|0.05%     \n",
      "     137|        53|CASH SHORTAGE  |$890379|$-1000000.00|1.68%     \n",
      "     138|        52|CASH SHORTAGE  |$908069|$-1000000.00|1.01%     \n",
      "     139|        51|CASH SHORTAGE  |$939247|$-1000000.00|0.93%     \n",
      "     140|        56|CASH SHORTAGE  |$944056|$-1000000.00|0.24%     \n",
      "     141|        57|CASH SHORTAGE  |$972713|$-1000000.00|1.82%     \n",
      "     142|        57|CASH SHORTAGE  |$973038|$-1000000.00|0.28%     \n",
      "     143|        56|CASH SHORTAGE  |$943289|$-1000000.00|0.63%     \n",
      "     144|        58|CASH SHORTAGE  |$954514|$-1000000.00|1.99%     \n",
      "     145|        54|CASH SHORTAGE  |$942582|$-1000000.00|1.13%     \n",
      "     146|        57|CASH SHORTAGE  |$974522|$-1000000.00|0.60%     \n",
      "     147|        58|CASH SHORTAGE  |$955262|$-1000000.00|0.92%     \n",
      "     148|        59|CASH SHORTAGE  |$930288|$-1000000.00|1.46%     \n",
      "     149|        58|CASH SHORTAGE  |$959049|$-1000000.00|1.67%     \n",
      "     150|        61|CASH SHORTAGE  |$940387|$-1000000.00|1.34%     \n",
      "     151|        70|CASH SHORTAGE  |$989578|$-1000000.00|0.66%     \n",
      "     152|        73|CASH SHORTAGE  |$977526|$-1000000.00|1.14%     \n",
      "     153|        78|CASH SHORTAGE  |$995578|$-1000000.00|1.27%     \n",
      "     154|        84|CASH SHORTAGE  |$1057574|$-1000000.00|1.54%     \n",
      "     155|        80|CASH SHORTAGE  |$1035439|$-1000000.00|0.93%     \n",
      "     156|        78|CASH SHORTAGE  |$1000429|$-1000000.00|1.13%     \n",
      "     157|        74|CASH SHORTAGE  |$998307|$-1000000.00|0.16%     \n",
      "     158|        74|CASH SHORTAGE  |$993215|$-1000000.00|0.55%     \n",
      "     159|        72|CASH SHORTAGE  |$1014417|$-1000000.00|0.39%     \n",
      "     160|        73|CASH SHORTAGE  |$990401|$-1000000.00|0.41%     \n",
      "     161|        73|CASH SHORTAGE  |$987687|$-1000000.00|1.69%     \n",
      "     162|        74|CASH SHORTAGE  |$987762|$-1000000.00|0.26%     \n",
      "     163|        72|CASH SHORTAGE  |$1008045|$-1000000.00|0.01%     \n",
      "     164|        79|CASH SHORTAGE  |$1001562|$-1000000.00|0.36%     \n",
      "     165|        90|CASH SHORTAGE  |$1064185|$-1000000.00|0.47%     \n",
      "     166|        87|CASH SHORTAGE  |$1100733|$-1000000.00|1.23%     \n",
      "     167|        89|CASH SHORTAGE  |$1080846|$-1000000.00|0.12%     \n",
      "     168|        92|CASH SHORTAGE  |$1070773|$-1000000.00|0.23%     \n",
      "     169|        94|CASH SHORTAGE  |$1102762|$-1000000.00|0.16%     \n",
      "     170|        95|CASH SHORTAGE  |$1117420|$-1000000.00|0.92%     \n",
      "     171|        84|CASH SHORTAGE  |$1065024|$-1000000.00|0.52%     \n",
      "     172|        87|CASH SHORTAGE  |$1118305|$-1000000.00|0.42%     \n",
      "     173|        87|CASH SHORTAGE  |$1118164|$-1000000.00|0.92%     \n",
      "     174|        93|CASH SHORTAGE  |$1111271|$-1000000.00|0.49%     \n",
      "     175|        95|CASH SHORTAGE  |$1122213|$-1000000.00|0.52%     \n",
      "     176|        90|CASH SHORTAGE  |$1075132|$-1000000.00|0.88%     \n",
      "     177|        80|CASH SHORTAGE  |$1022499|$-1000000.00|0.79%     \n",
      "     178|        84|CASH SHORTAGE  |$1053055|$-1000000.00|0.70%     \n",
      "     179|        75|CASH SHORTAGE  |$977613|$-1000000.00|1.05%     \n",
      "     180|        78|CASH SHORTAGE  |$991616|$-1000000.00|1.54%     \n",
      "     181|        82|CASH SHORTAGE  |$1037685|$-1000000.00|1.01%     \n",
      "     182|        82|CASH SHORTAGE  |$1024467|$-1000000.00|0.12%     \n",
      "     183|        76|CASH SHORTAGE  |$991228|$-1000000.00|1.46%     \n",
      "     184|        85|CASH SHORTAGE  |$1057811|$-1000000.00|1.86%     \n",
      "     185|        89|CASH SHORTAGE  |$1090874|$-1000000.00|0.44%     \n",
      "     186|        76|CASH SHORTAGE  |$986428|$-1000000.00|0.50%     \n",
      "     187|        86|CASH SHORTAGE  |$1072200|$-1000000.00|0.16%     \n",
      "     188|        85|CASH SHORTAGE  |$1044827|$-1000000.00|1.05%     \n",
      "     189|        93|CASH SHORTAGE  |$1092067|$-1000000.00|0.20%     \n",
      "     190|        85|CASH SHORTAGE  |$1070737|$-1000000.00|0.59%     \n",
      "     191|        83|CASH SHORTAGE  |$1063515|$-1000000.00|1.49%     \n",
      "     192|        80|CASH SHORTAGE  |$1031577|$-1000000.00|0.70%     \n",
      "     193|        90|CASH SHORTAGE  |$1069265|$-1000000.00|1.00%     \n",
      "     194|        90|CASH SHORTAGE  |$1081204|$-1000000.00|0.29%     \n",
      "     195|        89|CASH SHORTAGE  |$1110781|$-1000000.00|0.27%     \n",
      "     196|        90|CASH SHORTAGE  |$1089583|$-1000000.00|0.81%     \n",
      "     197|        92|CASH SHORTAGE  |$1083581|$-1000000.00|0.74%     \n",
      "     198|        96|CASH SHORTAGE  |$1106991|$-1000000.00|0.17%     \n",
      "     199|        93|CASH SHORTAGE  |$1137238|$-1000000.00|0.60%     \n",
      "     200|        88|CASH SHORTAGE  |$1119204|$-1000000.00|0.32%     \n",
      "     201|        88|CASH SHORTAGE  |$1110373|$-1000000.00|0.14%     \n",
      "     202|        85|CASH SHORTAGE  |$1084644|$-1000000.00|0.80%     \n",
      "     203|        82|CASH SHORTAGE  |$1047535|$-1000000.00|0.23%     \n",
      "     204|        77|CASH SHORTAGE  |$1013588|$-1000000.00|1.08%     \n",
      "     205|        84|CASH SHORTAGE  |$1080234|$-1000000.00|0.00%     \n",
      "     206|        84|CASH SHORTAGE  |$1071928|$-1000000.00|1.70%     \n",
      "     207|        85|CASH SHORTAGE  |$1076424|$-1000000.00|0.95%     \n",
      "     208|        94|CASH SHORTAGE  |$1113525|$-1000000.00|0.84%     \n",
      "     209|        80|CASH SHORTAGE  |$1051908|$-1000000.00|1.18%     \n",
      "     210|        75|CASH SHORTAGE  |$993556|$-1000000.00|0.13%     \n",
      "     211|        76|CASH SHORTAGE  |$997805|$-1000000.00|0.54%     \n",
      "     212|        85|CASH SHORTAGE  |$1080193|$-1000000.00|0.81%     \n",
      "     213|        83|CASH SHORTAGE  |$1070767|$-1000000.00|0.33%     \n",
      "     214|        80|CASH SHORTAGE  |$1053774|$-1000000.00|0.25%     \n",
      "     215|        85|CASH SHORTAGE  |$1093573|$-1000000.00|0.34%     \n",
      "     216|        82|CASH SHORTAGE  |$1059467|$-1000000.00|0.66%     \n",
      "     217|        86|CASH SHORTAGE  |$1113519|$-1000000.00|0.78%     \n",
      "     218|        83|CASH SHORTAGE  |$1088884|$-1000000.00|0.41%     \n",
      "     219|        90|CASH SHORTAGE  |$1089409|$-1000000.00|0.19%     \n",
      "     220|        82|CASH SHORTAGE  |$1087303|$-1000000.00|0.34%     \n",
      "     221|        82|CASH SHORTAGE  |$1078605|$-1000000.00|0.13%     \n",
      "     222|        86|CASH SHORTAGE  |$1129218|$-1000000.00|0.79%     \n",
      "     223|        78|CASH SHORTAGE  |$1048275|$-1000000.00|0.08%     \n",
      "     224|        75|CASH SHORTAGE  |$1013718|$-1000000.00|1.02%     \n",
      "     225|        78|CASH SHORTAGE  |$1062585|$-1000000.00|0.90%     \n",
      "     226|        78|CASH SHORTAGE  |$1058052|$-1000000.00|1.10%     \n",
      "     227|        80|CASH SHORTAGE  |$1084680|$-1000000.00|1.40%     \n",
      "     228|        73|CASH SHORTAGE  |$1035345|$-1000000.00|0.83%     \n",
      "     229|        75|CASH SHORTAGE  |$1026598|$-1000000.00|0.66%     \n",
      "     230|        74|CASH SHORTAGE  |$1028533|$-1000000.00|1.11%     \n",
      "     231|        69|CASH SHORTAGE  |$1005220|$-1000000.00|0.27%     \n",
      "     232|        69|CASH SHORTAGE  |$1005765|$-1000000.00|0.65%     \n",
      "     233|        67|CASH SHORTAGE  |$1020446|$-1000000.00|1.32%     \n",
      "     234|        69|CASH SHORTAGE  |$1001584|$-1000000.00|1.11%     \n",
      "     235|        67|CASH SHORTAGE  |$1024094|$-1000000.00|0.71%     \n",
      "     236|        67|CASH SHORTAGE  |$1023555|$-1000000.00|0.63%     \n",
      "     237|        66|CASH SHORTAGE  |$991796|$-1000000.00|0.06%     \n",
      "     238|        67|CASH SHORTAGE  |$1027329|$-1000000.00|1.57%     \n",
      "     239|        65|CASH SHORTAGE  |$986347|$-1000000.00|1.60%     \n",
      "     240|        65|CASH SHORTAGE  |$986014|$-1000000.00|1.24%     \n",
      "     241|        65|CASH SHORTAGE  |$980910|$-1000000.00|0.15%     \n",
      "     242|        67|CASH SHORTAGE  |$1024159|$-1000000.00|1.04%     \n",
      "     243|        71|CASH SHORTAGE  |$1056786|$-1000000.00|0.81%     \n",
      "     244|        69|CASH SHORTAGE  |$1006841|$-1000000.00|0.15%     \n",
      "     245|        67|CASH SHORTAGE  |$1027984|$-1000000.00|0.82%     \n",
      "     246|        67|CASH SHORTAGE  |$1034561|$-1000000.00|0.66%     \n",
      "     247|        65|CASH SHORTAGE  |$992790|$-1000000.00|1.18%     \n",
      "     248|        64|CASH SHORTAGE  |$1001693|$-1000000.00|1.42%     \n",
      "     249|        65|CASH SHORTAGE  |$986411|$-1000000.00|0.56%     \n",
      "     250|        63|CASH SHORTAGE  |$994095|$-1000000.00|1.24%     \n",
      "     251|        67|CASH SHORTAGE  |$1034510|$-1000000.00|0.97%     \n",
      "     252|        65|CASH SHORTAGE  |$990539|$-1000000.00|1.06%     \n",
      "     253|        66|CASH SHORTAGE  |$990856|$-1000000.00|1.19%     \n",
      "     254|        67|CASH SHORTAGE  |$1039220|$-1000000.00|0.34%     \n",
      "     255|        66|CASH SHORTAGE  |$993735|$-1000000.00|0.10%     \n",
      "     256|        62|CASH SHORTAGE  |$996479|$-1000000.00|0.82%     \n",
      "     257|        61|CASH SHORTAGE  |$961801|$-1000000.00|0.13%     \n",
      "     258|        61|CASH SHORTAGE  |$959376|$-1000000.00|0.92%     \n",
      "     259|        60|CASH SHORTAGE  |$937814|$-1000000.00|0.96%     \n",
      "     260|        64|CASH SHORTAGE  |$991333|$-1000000.00|0.24%     \n",
      "     261|        63|CASH SHORTAGE  |$989296|$-1000000.00|0.55%     \n",
      "     262|        62|CASH SHORTAGE  |$991405|$-1000000.00|1.24%     \n",
      "     263|        61|CASH SHORTAGE  |$955530|$-1000000.00|0.53%     \n",
      "     264|        60|CASH SHORTAGE  |$939272|$-1000000.00|0.31%     \n",
      "     265|        60|CASH SHORTAGE  |$934742|$-1000000.00|0.25%     \n",
      "     266|        60|CASH SHORTAGE  |$935499|$-1000000.00|0.24%     \n",
      "     267|        59|CASH SHORTAGE  |$922414|$-1000000.00|1.49%     \n",
      "     268|        58|CASH SHORTAGE  |$953735|$-1000000.00|1.17%     \n",
      "     269|        58|CASH SHORTAGE  |$956530|$-1000000.00|0.83%     \n",
      "     270|        59|CASH SHORTAGE  |$923011|$-1000000.00|0.52%     \n",
      "     271|        61|CASH SHORTAGE  |$959088|$-1000000.00|0.14%     \n",
      "     272|        59|CASH SHORTAGE  |$922373|$-1000000.00|1.07%     \n",
      "     273|        63|CASH SHORTAGE  |$991562|$-1000000.00|0.63%     \n",
      "     274|        63|CASH SHORTAGE  |$990199|$-1000000.00|1.11%     \n",
      "     275|        66|CASH SHORTAGE  |$989509|$-1000000.00|1.20%     \n",
      "     276|        67|CASH SHORTAGE  |$1035401|$-1000000.00|0.96%     \n",
      "     277|        69|CASH SHORTAGE  |$1014286|$-1000000.00|1.10%     \n",
      "     278|        69|CASH SHORTAGE  |$1015318|$-1000000.00|0.94%     \n",
      "     279|        67|CASH SHORTAGE  |$1036837|$-1000000.00|1.25%     \n",
      "     280|        68|CASH SHORTAGE  |$1041623|$-1000000.00|0.64%     \n",
      "     281|        67|CASH SHORTAGE  |$1038236|$-1000000.00|1.08%     \n",
      "     282|        67|CASH SHORTAGE  |$1037758|$-1000000.00|0.78%     \n",
      "     283|        67|CASH SHORTAGE  |$1036280|$-1000000.00|0.52%     \n",
      "     284|        65|CASH SHORTAGE  |$989036|$-1000000.00|1.13%     \n",
      "     285|        64|CASH SHORTAGE  |$991301|$-1000000.00|0.20%     \n",
      "     286|        61|CASH SHORTAGE  |$963309|$-1000000.00|1.33%     \n",
      "     287|        60|CASH SHORTAGE  |$939711|$-1000000.00|1.37%     \n",
      "     288|        59|CASH SHORTAGE  |$926126|$-1000000.00|0.81%     \n",
      "     289|        59|CASH SHORTAGE  |$930020|$-1000000.00|0.67%     \n",
      "     290|        58|CASH SHORTAGE  |$965941|$-1000000.00|0.95%     \n",
      "     291|        57|CASH SHORTAGE  |$980243|$-1000000.00|0.63%     \n",
      "     292|        58|CASH SHORTAGE  |$959602|$-1000000.00|0.16%     \n",
      "     293|        53|CASH SHORTAGE  |$899323|$-1000000.00|1.37%     \n",
      "     294|        55|CASH SHORTAGE  |$941622|$-1000000.00|0.75%     \n",
      "     295|        54|CASH SHORTAGE  |$965375|$-1000000.00|0.36%     \n",
      "     296|        53|CASH SHORTAGE  |$899101|$-1000000.00|1.80%     \n",
      "     297|        57|CASH SHORTAGE  |$979429|$-1000000.00|0.35%     \n",
      "     298|        54|CASH SHORTAGE  |$963140|$-1000000.00|1.93%     \n",
      "     299|        58|CASH SHORTAGE  |$958595|$-1000000.00|0.10%     \n",
      "     300|        55|CASH SHORTAGE  |$938375|$-1000000.00|0.74%     \n",
      "     301|        56|CASH SHORTAGE  |$956171|$-1000000.00|0.45%     \n",
      "     302|        57|CASH SHORTAGE  |$974953|$-1000000.00|0.22%     \n",
      "     303|        57|CASH SHORTAGE  |$974000|$-1000000.00|1.62%     \n",
      "     304|        56|CASH SHORTAGE  |$952693|$-1000000.00|0.57%     \n",
      "     305|        54|CASH SHORTAGE  |$962283|$-1000000.00|0.70%     \n",
      "     306|        55|CASH SHORTAGE  |$939428|$-1000000.00|0.30%     \n",
      "     307|        55|CASH SHORTAGE  |$938878|$-1000000.00|0.78%     \n",
      "     308|        55|CASH SHORTAGE  |$936711|$-1000000.00|0.28%     \n",
      "     309|        56|CASH SHORTAGE  |$953989|$-1000000.00|0.49%     \n",
      "     310|        54|CASH SHORTAGE  |$963251|$-1000000.00|1.94%     \n",
      "     311|        56|CASH SHORTAGE  |$957281|$-1000000.00|0.92%     \n",
      "     312|        56|CASH SHORTAGE  |$958302|$-1000000.00|0.11%     \n",
      "     313|        55|CASH SHORTAGE  |$940008|$-1000000.00|0.92%     \n",
      "     314|        56|CASH SHORTAGE  |$955872|$-1000000.00|0.20%     \n",
      "     315|        54|CASH SHORTAGE  |$967654|$-1000000.00|0.95%     \n",
      "     316|        54|CASH SHORTAGE  |$966987|$-1000000.00|0.93%     \n",
      "     317|        56|CASH SHORTAGE  |$956592|$-1000000.00|0.51%     \n",
      "     318|        57|CASH SHORTAGE  |$979370|$-1000000.00|0.69%     \n",
      "     319|        54|CASH SHORTAGE  |$963745|$-1000000.00|1.55%     \n",
      "     320|        54|CASH SHORTAGE  |$967261|$-1000000.00|1.44%     \n",
      "     321|        55|CASH SHORTAGE  |$943846|$-1000000.00|0.43%     \n",
      "     322|        56|CASH SHORTAGE  |$956931|$-1000000.00|0.18%     \n",
      "     323|        56|CASH SHORTAGE  |$959156|$-1000000.00|1.18%     \n",
      "     324|        55|CASH SHORTAGE  |$940736|$-1000000.00|1.83%     \n",
      "     325|        54|CASH SHORTAGE  |$963601|$-1000000.00|1.50%     \n",
      "     326|        54|CASH SHORTAGE  |$967273|$-1000000.00|0.90%     \n",
      "     327|        53|CASH SHORTAGE  |$901535|$-1000000.00|1.48%     \n",
      "     328|        51|CASH SHORTAGE  |$940406|$-1000000.00|0.78%     \n",
      "     329|        52|CASH SHORTAGE  |$918434|$-1000000.00|0.84%     \n",
      "     330|        55|CASH SHORTAGE  |$936380|$-1000000.00|1.29%     \n",
      "     331|        56|CASH SHORTAGE  |$950692|$-1000000.00|1.09%     \n",
      "     332|        57|CASH SHORTAGE  |$979615|$-1000000.00|1.64%     \n",
      "     333|        56|CASH SHORTAGE  |$956304|$-1000000.00|1.36%     \n",
      "     334|        56|CASH SHORTAGE  |$958092|$-1000000.00|0.25%     \n",
      "     335|        55|CASH SHORTAGE  |$942197|$-1000000.00|0.53%     \n",
      "     336|        59|CASH SHORTAGE  |$925430|$-1000000.00|0.56%     \n",
      "     337|        57|CASH SHORTAGE  |$979827|$-1000000.00|0.84%     \n",
      "     338|        61|CASH SHORTAGE  |$968125|$-1000000.00|0.85%     \n",
      "     339|        61|CASH SHORTAGE  |$969282|$-1000000.00|0.05%     \n",
      "     340|        61|CASH SHORTAGE  |$964917|$-1000000.00|0.81%     \n",
      "     341|        61|CASH SHORTAGE  |$964754|$-1000000.00|1.26%     \n",
      "     342|        61|CASH SHORTAGE  |$964599|$-1000000.00|0.17%     \n",
      "     343|        61|CASH SHORTAGE  |$965584|$-1000000.00|0.50%     \n",
      "     344|        61|CASH SHORTAGE  |$964587|$-1000000.00|1.32%     \n",
      "     345|        61|CASH SHORTAGE  |$964833|$-1000000.00|1.08%     \n",
      "     346|        62|CASH SHORTAGE  |$994952|$-1000000.00|0.41%     \n",
      "     347|        61|CASH SHORTAGE  |$965732|$-1000000.00|1.37%     \n",
      "     348|        60|CASH SHORTAGE  |$943096|$-1000000.00|0.84%     \n",
      "     349|        60|CASH SHORTAGE  |$943331|$-1000000.00|1.02%     \n",
      "     350|        60|CASH SHORTAGE  |$943718|$-1000000.00|0.04%     \n",
      "     351|        60|CASH SHORTAGE  |$942910|$-1000000.00|0.88%     \n",
      "     352|        60|CASH SHORTAGE  |$942186|$-1000000.00|0.54%     \n",
      "     353|        60|CASH SHORTAGE  |$943523|$-1000000.00|0.37%     \n",
      "     354|        59|CASH SHORTAGE  |$928872|$-1000000.00|1.53%     \n",
      "     355|        60|CASH SHORTAGE  |$945220|$-1000000.00|0.64%     \n",
      "     356|        59|CASH SHORTAGE  |$928850|$-1000000.00|1.65%     \n",
      "     357|        60|CASH SHORTAGE  |$944679|$-1000000.00|0.04%     \n",
      "     358|        60|CASH SHORTAGE  |$944113|$-1000000.00|0.23%     \n",
      "     359|        60|CASH SHORTAGE  |$942744|$-1000000.00|1.31%     \n",
      "     360|        60|CASH SHORTAGE  |$945941|$-1000000.00|0.88%     \n",
      "     361|        60|CASH SHORTAGE  |$943182|$-1000000.00|0.68%     \n",
      "     362|        59|CASH SHORTAGE  |$928041|$-1000000.00|1.35%     \n",
      "     363|        59|CASH SHORTAGE  |$928848|$-1000000.00|1.45%     \n",
      "     364|        58|CASH SHORTAGE  |$959763|$-1000000.00|1.28%     \n",
      "     365|        58|CASH SHORTAGE  |$960883|$-1000000.00|1.15%     \n",
      "     366|        56|CASH SHORTAGE  |$957324|$-1000000.00|0.71%     \n",
      "     367|        57|CASH SHORTAGE  |$981832|$-1000000.00|0.91%     \n",
      "     368|        58|CASH SHORTAGE  |$963569|$-1000000.00|1.29%     \n",
      "     369|        57|CASH SHORTAGE  |$979846|$-1000000.00|1.18%     \n",
      "     370|        57|CASH SHORTAGE  |$980813|$-1000000.00|0.94%     \n",
      "     371|        56|CASH SHORTAGE  |$957526|$-1000000.00|0.70%     \n",
      "     372|        60|CASH SHORTAGE  |$944340|$-1000000.00|0.30%     \n",
      "     373|        59|CASH SHORTAGE  |$928860|$-1000000.00|1.50%     \n",
      "     374|        58|CASH SHORTAGE  |$962076|$-1000000.00|0.34%     \n",
      "     375|        57|CASH SHORTAGE  |$979583|$-1000000.00|0.58%     \n",
      "     376|        58|CASH SHORTAGE  |$960121|$-1000000.00|1.07%     \n",
      "     377|        60|CASH SHORTAGE  |$941567|$-1000000.00|0.05%     \n",
      "     378|        59|CASH SHORTAGE  |$928772|$-1000000.00|0.72%     \n",
      "     379|        58|CASH SHORTAGE  |$963423|$-1000000.00|0.28%     \n",
      "     380|        58|CASH SHORTAGE  |$961510|$-1000000.00|1.42%     \n",
      "     381|        57|CASH SHORTAGE  |$981010|$-1000000.00|0.02%     \n",
      "     382|        57|CASH SHORTAGE  |$980435|$-1000000.00|1.36%     \n",
      "     383|        58|CASH SHORTAGE  |$961256|$-1000000.00|0.59%     \n",
      "     384|        60|CASH SHORTAGE  |$946136|$-1000000.00|1.08%     \n",
      "     385|        61|CASH SHORTAGE  |$969113|$-1000000.00|0.61%     \n",
      "     386|        60|CASH SHORTAGE  |$946357|$-1000000.00|0.26%     \n",
      "     387|        60|CASH SHORTAGE  |$945042|$-1000000.00|0.00%     \n",
      "     388|        59|CASH SHORTAGE  |$928253|$-1000000.00|0.68%     \n",
      "     389|        58|CASH SHORTAGE  |$961961|$-1000000.00|0.58%     \n",
      "     390|        58|CASH SHORTAGE  |$958736|$-1000000.00|1.75%     \n",
      "     391|        61|CASH SHORTAGE  |$966621|$-1000000.00|1.21%     \n",
      "     392|        61|CASH SHORTAGE  |$966975|$-1000000.00|0.97%     \n",
      "     393|        60|CASH SHORTAGE  |$943414|$-1000000.00|0.07%     \n",
      "     394|        60|CASH SHORTAGE  |$943607|$-1000000.00|0.47%     \n",
      "     395|        61|CASH SHORTAGE  |$967557|$-1000000.00|1.13%     \n",
      "     396|        60|CASH SHORTAGE  |$943998|$-1000000.00|0.64%     \n",
      "     397|        60|CASH SHORTAGE  |$945600|$-1000000.00|1.24%     \n",
      "     398|        60|CASH SHORTAGE  |$943832|$-1000000.00|0.87%     \n",
      "     399|        61|CASH SHORTAGE  |$968752|$-1000000.00|0.74%     \n",
      "     400|        60|CASH SHORTAGE  |$944390|$-1000000.00|0.39%     \n",
      "     401|        60|CASH SHORTAGE  |$944814|$-1000000.00|0.06%     \n",
      "     402|        60|CASH SHORTAGE  |$944900|$-1000000.00|1.19%     \n",
      "     403|        60|CASH SHORTAGE  |$943744|$-1000000.00|0.08%     \n",
      "     404|        60|CASH SHORTAGE  |$943712|$-1000000.00|0.72%     \n",
      "     405|        60|CASH SHORTAGE  |$944101|$-1000000.00|0.32%     \n",
      "     406|        59|CASH SHORTAGE  |$930264|$-1000000.00|1.75%     \n",
      "     407|        60|CASH SHORTAGE  |$944969|$-1000000.00|0.48%     \n",
      "     408|        59|CASH SHORTAGE  |$929010|$-1000000.00|1.84%     \n",
      "     409|        59|CASH SHORTAGE  |$929236|$-1000000.00|1.25%     \n",
      "     410|        60|CASH SHORTAGE  |$941321|$-1000000.00|0.52%     \n",
      "     411|        60|CASH SHORTAGE  |$943749|$-1000000.00|0.99%     \n",
      "     412|        57|CASH SHORTAGE  |$979897|$-1000000.00|1.33%     \n",
      "     413|        58|CASH SHORTAGE  |$961014|$-1000000.00|0.29%     \n",
      "     414|        59|CASH SHORTAGE  |$928189|$-1000000.00|0.91%     \n",
      "     415|        59|CASH SHORTAGE  |$930592|$-1000000.00|0.29%     \n",
      "     416|        59|CASH SHORTAGE  |$930496|$-1000000.00|1.22%     \n",
      "     417|        59|CASH SHORTAGE  |$930117|$-1000000.00|1.04%     \n",
      "     418|        59|CASH SHORTAGE  |$929508|$-1000000.00|0.22%     \n",
      "     419|        59|CASH SHORTAGE  |$929846|$-1000000.00|0.08%     \n",
      "     420|        60|CASH SHORTAGE  |$944775|$-1000000.00|0.06%     \n",
      "     421|        59|CASH SHORTAGE  |$930046|$-1000000.00|0.08%     \n",
      "     422|        59|CASH SHORTAGE  |$929436|$-1000000.00|1.28%     \n",
      "     423|        59|CASH SHORTAGE  |$930667|$-1000000.00|1.20%     \n",
      "     424|        59|CASH SHORTAGE  |$930344|$-1000000.00|1.40%     \n",
      "     425|        59|CASH SHORTAGE  |$930335|$-1000000.00|0.96%     \n",
      "     426|        59|CASH SHORTAGE  |$930365|$-1000000.00|0.64%     \n",
      "     427|        58|CASH SHORTAGE  |$961343|$-1000000.00|1.54%     \n",
      "     428|        59|CASH SHORTAGE  |$929909|$-1000000.00|0.63%     \n",
      "     429|        59|CASH SHORTAGE  |$929282|$-1000000.00|1.30%     \n",
      "     430|        60|CASH SHORTAGE  |$946941|$-1000000.00|0.10%     \n",
      "     431|        62|CASH SHORTAGE  |$997553|$-1000000.00|0.35%     \n",
      "     432|        62|CASH SHORTAGE  |$1001481|$-1000000.00|1.00%     \n",
      "     433|        61|CASH SHORTAGE  |$968912|$-1000000.00|0.80%     \n",
      "     434|        63|CASH SHORTAGE  |$1005891|$-1000000.00|0.98%     \n",
      "     435|        61|CASH SHORTAGE  |$968563|$-1000000.00|0.16%     \n",
      "     436|        63|CASH SHORTAGE  |$1006307|$-1000000.00|0.59%     \n",
      "     437|        63|CASH SHORTAGE  |$1005445|$-1000000.00|0.89%     \n",
      "     438|        61|CASH SHORTAGE  |$969505|$-1000000.00|1.66%     \n",
      "     439|        61|CASH SHORTAGE  |$969695|$-1000000.00|0.48%     \n",
      "     440|        63|CASH SHORTAGE  |$1006648|$-1000000.00|0.26%     \n",
      "     441|        62|CASH SHORTAGE  |$1002072|$-1000000.00|0.93%     \n",
      "     442|        61|CASH SHORTAGE  |$970779|$-1000000.00|0.42%     \n",
      "     443|        64|CASH SHORTAGE  |$1005606|$-1000000.00|0.07%     \n",
      "     444|        64|CASH SHORTAGE  |$1004572|$-1000000.00|0.83%     \n",
      "     445|        62|CASH SHORTAGE  |$1002588|$-1000000.00|1.64%     \n",
      "     446|        60|CASH SHORTAGE  |$945816|$-1000000.00|0.84%     \n",
      "     447|        61|CASH SHORTAGE  |$969861|$-1000000.00|0.46%     \n",
      "     448|        58|CASH SHORTAGE  |$962002|$-1000000.00|1.31%     \n",
      "     449|        59|CASH SHORTAGE  |$931230|$-1000000.00|0.36%     \n",
      "     450|        58|CASH SHORTAGE  |$962406|$-1000000.00|1.57%     \n",
      "     451|        58|CASH SHORTAGE  |$961965|$-1000000.00|1.36%     \n",
      "     452|        58|CASH SHORTAGE  |$962024|$-1000000.00|1.66%     \n",
      "     453|        59|CASH SHORTAGE  |$931143|$-1000000.00|0.54%     \n",
      "     454|        59|CASH SHORTAGE  |$930345|$-1000000.00|0.87%     \n",
      "     455|        60|CASH SHORTAGE  |$946476|$-1000000.00|0.98%     \n",
      "     456|        58|CASH SHORTAGE  |$962251|$-1000000.00|1.44%     \n",
      "     457|        58|CASH SHORTAGE  |$961266|$-1000000.00|1.59%     \n",
      "     458|        58|CASH SHORTAGE  |$961561|$-1000000.00|0.74%     \n",
      "     459|        59|CASH SHORTAGE  |$932259|$-1000000.00|0.14%     \n",
      "     460|        58|CASH SHORTAGE  |$962411|$-1000000.00|0.21%     \n",
      "     461|        58|CASH SHORTAGE  |$961842|$-1000000.00|0.89%     \n",
      "     462|        58|CASH SHORTAGE  |$962729|$-1000000.00|1.45%     \n",
      "     463|        57|CASH SHORTAGE  |$979879|$-1000000.00|1.73%     \n",
      "     464|        57|CASH SHORTAGE  |$979443|$-1000000.00|0.30%     \n",
      "     465|        58|CASH SHORTAGE  |$962627|$-1000000.00|0.32%     \n",
      "     466|        57|CASH SHORTAGE  |$982822|$-1000000.00|0.02%     \n",
      "     467|        58|CASH SHORTAGE  |$961702|$-1000000.00|0.22%     \n",
      "     468|        58|CASH SHORTAGE  |$963499|$-1000000.00|1.19%     \n",
      "     469|        59|CASH SHORTAGE  |$930441|$-1000000.00|0.12%     \n",
      "     470|        57|CASH SHORTAGE  |$980452|$-1000000.00|1.93%     \n",
      "     471|        58|CASH SHORTAGE  |$962008|$-1000000.00|0.65%     \n",
      "     472|        57|CASH SHORTAGE  |$982399|$-1000000.00|1.94%     \n",
      "     473|        58|CASH SHORTAGE  |$962309|$-1000000.00|0.83%     \n",
      "     474|        58|CASH SHORTAGE  |$962645|$-1000000.00|1.36%     \n",
      "     475|        59|CASH SHORTAGE  |$931720|$-1000000.00|1.28%     \n",
      "     476|        58|CASH SHORTAGE  |$962013|$-1000000.00|1.13%     \n",
      "     477|        57|CASH SHORTAGE  |$981273|$-1000000.00|1.54%     \n",
      "     478|        57|CASH SHORTAGE  |$981376|$-1000000.00|1.50%     \n",
      "     479|        58|CASH SHORTAGE  |$962801|$-1000000.00|0.48%     \n",
      "     480|        58|CASH SHORTAGE  |$964125|$-1000000.00|0.86%     \n",
      "     481|        59|CASH SHORTAGE  |$931950|$-1000000.00|0.40%     \n",
      "     482|        59|CASH SHORTAGE  |$931412|$-1000000.00|1.27%     \n",
      "     483|        58|CASH SHORTAGE  |$961410|$-1000000.00|1.19%     \n",
      "     484|        57|CASH SHORTAGE  |$981311|$-1000000.00|1.90%     \n",
      "     485|        58|CASH SHORTAGE  |$961488|$-1000000.00|0.58%     \n",
      "     486|        59|CASH SHORTAGE  |$928758|$-1000000.00|1.15%     \n",
      "     487|        58|CASH SHORTAGE  |$962233|$-1000000.00|1.62%     \n",
      "     488|        58|CASH SHORTAGE  |$960269|$-1000000.00|0.94%     \n",
      "     489|        58|CASH SHORTAGE  |$961139|$-1000000.00|0.37%     \n",
      "     490|        58|CASH SHORTAGE  |$961777|$-1000000.00|1.12%     \n",
      "     491|        57|CASH SHORTAGE  |$980128|$-1000000.00|1.44%     \n",
      "     492|        58|CASH SHORTAGE  |$963036|$-1000000.00|0.79%     \n",
      "     493|        57|CASH SHORTAGE  |$980310|$-1000000.00|1.00%     \n",
      "     494|        58|CASH SHORTAGE  |$963470|$-1000000.00|0.29%     \n",
      "     495|        57|CASH SHORTAGE  |$980477|$-1000000.00|0.95%     \n",
      "     496|        57|CASH SHORTAGE  |$980161|$-1000000.00|1.03%     \n",
      "     497|        57|CASH SHORTAGE  |$980608|$-1000000.00|0.72%     \n",
      "     498|        57|CASH SHORTAGE  |$980226|$-1000000.00|0.87%     \n",
      "     499|        57|CASH SHORTAGE  |$980171|$-1000000.00|1.13%     \n",
      "     500|        57|CASH SHORTAGE  |$980945|$-1000000.00|1.24%     \n",
      "     501|        57|CASH SHORTAGE  |$980274|$-1000000.00|1.28%     \n",
      "     502|        57|CASH SHORTAGE  |$980241|$-1000000.00|0.62%     \n",
      "     503|        57|CASH SHORTAGE  |$981568|$-1000000.00|1.03%     \n",
      "     504|        56|CASH SHORTAGE  |$955837|$-1000000.00|1.25%     \n",
      "     505|        56|CASH SHORTAGE  |$956993|$-1000000.00|1.02%     \n",
      "     506|        56|CASH SHORTAGE  |$958014|$-1000000.00|1.74%     \n",
      "     507|        56|CASH SHORTAGE  |$956407|$-1000000.00|0.61%     \n",
      "     508|        56|CASH SHORTAGE  |$958181|$-1000000.00|1.46%     \n",
      "     509|        56|CASH SHORTAGE  |$957563|$-1000000.00|0.61%     \n",
      "     510|        56|CASH SHORTAGE  |$957624|$-1000000.00|1.64%     \n",
      "     511|        56|CASH SHORTAGE  |$958293|$-1000000.00|0.84%     \n",
      "     512|        56|CASH SHORTAGE  |$957086|$-1000000.00|1.32%     \n",
      "     513|        57|CASH SHORTAGE  |$981042|$-1000000.00|0.36%     \n",
      "     514|        57|CASH SHORTAGE  |$980824|$-1000000.00|0.72%     \n",
      "     515|        57|CASH SHORTAGE  |$980811|$-1000000.00|0.89%     \n",
      "     516|        57|CASH SHORTAGE  |$980976|$-1000000.00|0.73%     \n",
      "     517|        57|CASH SHORTAGE  |$980602|$-1000000.00|1.19%     \n",
      "     518|        57|CASH SHORTAGE  |$979959|$-1000000.00|0.68%     \n",
      "     519|        57|CASH SHORTAGE  |$980405|$-1000000.00|0.84%     \n",
      "     520|        57|CASH SHORTAGE  |$979888|$-1000000.00|1.44%     \n",
      "     521|        57|CASH SHORTAGE  |$980921|$-1000000.00|0.33%     \n",
      "     522|        57|CASH SHORTAGE  |$980807|$-1000000.00|1.27%     \n",
      "     523|        57|CASH SHORTAGE  |$980320|$-1000000.00|1.11%     \n",
      "     524|        57|CASH SHORTAGE  |$979927|$-1000000.00|1.13%     \n",
      "     525|        57|CASH SHORTAGE  |$980734|$-1000000.00|1.17%     \n",
      "     526|        57|CASH SHORTAGE  |$980866|$-1000000.00|0.89%     \n",
      "     527|        57|CASH SHORTAGE  |$980813|$-1000000.00|0.78%     \n",
      "     528|        58|CASH SHORTAGE  |$962605|$-1000000.00|0.17%     \n",
      "     529|        57|CASH SHORTAGE  |$981112|$-1000000.00|0.95%     \n",
      "     530|        57|CASH SHORTAGE  |$979831|$-1000000.00|1.32%     \n",
      "     531|        57|CASH SHORTAGE  |$980816|$-1000000.00|0.92%     \n",
      "     532|        57|CASH SHORTAGE  |$980419|$-1000000.00|1.48%     \n",
      "     533|        57|CASH SHORTAGE  |$980707|$-1000000.00|0.79%     \n",
      "     534|        57|CASH SHORTAGE  |$981183|$-1000000.00|0.83%     \n",
      "     535|        57|CASH SHORTAGE  |$980946|$-1000000.00|1.45%     \n",
      "     536|        57|CASH SHORTAGE  |$980761|$-1000000.00|1.14%     \n",
      "     537|        57|CASH SHORTAGE  |$980603|$-1000000.00|1.26%     \n",
      "     538|        57|CASH SHORTAGE  |$980777|$-1000000.00|1.06%     \n",
      "     539|        57|CASH SHORTAGE  |$980815|$-1000000.00|0.91%     \n",
      "     540|        57|CASH SHORTAGE  |$980835|$-1000000.00|1.51%     \n",
      "     541|        57|CASH SHORTAGE  |$980700|$-1000000.00|1.13%     \n",
      "     542|        57|CASH SHORTAGE  |$981026|$-1000000.00|1.58%     \n",
      "     543|        57|CASH SHORTAGE  |$980549|$-1000000.00|1.06%     \n",
      "     544|        57|CASH SHORTAGE  |$980373|$-1000000.00|1.06%     \n",
      "     545|        57|CASH SHORTAGE  |$981226|$-1000000.00|0.65%     \n",
      "     546|        57|CASH SHORTAGE  |$980990|$-1000000.00|0.92%     \n",
      "     547|        57|CASH SHORTAGE  |$981003|$-1000000.00|0.81%     \n",
      "     548|        57|CASH SHORTAGE  |$980407|$-1000000.00|0.92%     \n",
      "     549|        57|CASH SHORTAGE  |$978980|$-1000000.00|1.13%     \n",
      "     550|        57|CASH SHORTAGE  |$980731|$-1000000.00|0.46%     \n",
      "     551|        56|CASH SHORTAGE  |$956144|$-1000000.00|1.81%     \n",
      "     552|        56|CASH SHORTAGE  |$956821|$-1000000.00|1.81%     \n",
      "     553|        56|CASH SHORTAGE  |$957923|$-1000000.00|1.31%     \n",
      "     554|        56|CASH SHORTAGE  |$957133|$-1000000.00|1.19%     \n",
      "     555|        57|CASH SHORTAGE  |$980327|$-1000000.00|0.89%     \n",
      "     556|        57|CASH SHORTAGE  |$980112|$-1000000.00|0.83%     \n",
      "     557|        57|CASH SHORTAGE  |$979798|$-1000000.00|0.40%     \n",
      "     558|        57|CASH SHORTAGE  |$981049|$-1000000.00|0.61%     \n",
      "     559|        57|CASH SHORTAGE  |$980833|$-1000000.00|0.59%     \n",
      "     560|        57|CASH SHORTAGE  |$981388|$-1000000.00|0.28%     \n",
      "     561|        57|CASH SHORTAGE  |$980727|$-1000000.00|0.48%     \n",
      "     562|        57|CASH SHORTAGE  |$981339|$-1000000.00|1.00%     \n",
      "     563|        57|CASH SHORTAGE  |$980718|$-1000000.00|0.03%     \n",
      "     564|        57|CASH SHORTAGE  |$981127|$-1000000.00|0.98%     \n",
      "     565|        57|CASH SHORTAGE  |$981610|$-1000000.00|0.08%     \n",
      "     566|        57|CASH SHORTAGE  |$979588|$-1000000.00|0.92%     \n",
      "     567|        57|CASH SHORTAGE  |$980439|$-1000000.00|1.78%     \n",
      "     568|        57|CASH SHORTAGE  |$982842|$-1000000.00|0.54%     \n",
      "     569|        57|CASH SHORTAGE  |$981926|$-1000000.00|0.76%     \n",
      "     570|        57|CASH SHORTAGE  |$980599|$-1000000.00|1.02%     \n",
      "     571|        57|CASH SHORTAGE  |$979682|$-1000000.00|1.35%     \n",
      "     572|        58|CASH SHORTAGE  |$965987|$-1000000.00|0.96%     \n",
      "     573|        56|CASH SHORTAGE  |$957966|$-1000000.00|1.01%     \n",
      "     574|        57|CASH SHORTAGE  |$981138|$-1000000.00|0.24%     \n",
      "     575|        57|CASH SHORTAGE  |$981669|$-1000000.00|0.38%     \n",
      "     576|        56|CASH SHORTAGE  |$958181|$-1000000.00|1.04%     \n",
      "     577|        57|CASH SHORTAGE  |$981265|$-1000000.00|0.32%     \n",
      "     578|        57|CASH SHORTAGE  |$980927|$-1000000.00|0.59%     \n",
      "     579|        55|CASH SHORTAGE  |$944814|$-1000000.00|1.60%     \n",
      "     580|        54|CASH SHORTAGE  |$966900|$-1000000.00|1.82%     \n",
      "     581|        55|CASH SHORTAGE  |$945668|$-1000000.00|1.44%     \n",
      "     582|        56|CASH SHORTAGE  |$956680|$-1000000.00|0.18%     \n",
      "     583|        55|CASH SHORTAGE  |$944445|$-1000000.00|0.14%     \n",
      "     584|        55|CASH SHORTAGE  |$946097|$-1000000.00|0.22%     \n",
      "     585|        55|CASH SHORTAGE  |$944999|$-1000000.00|0.73%     \n",
      "     586|        54|CASH SHORTAGE  |$967602|$-1000000.00|1.80%     \n",
      "     587|        54|CASH SHORTAGE  |$965853|$-1000000.00|0.94%     \n",
      "     588|        53|CASH SHORTAGE  |$907683|$-1000000.00|1.62%     \n",
      "     589|        53|CASH SHORTAGE  |$906998|$-1000000.00|0.80%     \n",
      "     590|        53|CASH SHORTAGE  |$906158|$-1000000.00|1.19%     \n",
      "     591|        54|CASH SHORTAGE  |$968538|$-1000000.00|0.18%     \n",
      "     592|        54|CASH SHORTAGE  |$968349|$-1000000.00|0.33%     \n",
      "     593|        53|CASH SHORTAGE  |$906489|$-1000000.00|1.13%     \n",
      "     594|        53|CASH SHORTAGE  |$906513|$-1000000.00|1.10%     \n",
      "     595|        53|CASH SHORTAGE  |$906916|$-1000000.00|0.95%     \n",
      "     596|        53|CASH SHORTAGE  |$906284|$-1000000.00|0.96%     \n",
      "     597|        53|CASH SHORTAGE  |$906135|$-1000000.00|1.53%     \n",
      "     598|        52|CASH SHORTAGE  |$922100|$-1000000.00|2.08%     \n",
      "     599|        53|CASH SHORTAGE  |$906579|$-1000000.00|1.12%     \n",
      "     600|        53|CASH SHORTAGE  |$905340|$-1000000.00|1.12%     \n",
      "     601|        53|CASH SHORTAGE  |$906132|$-1000000.00|0.46%     \n",
      "     602|        53|CASH SHORTAGE  |$905411|$-1000000.00|0.04%     \n",
      "     603|        52|CASH SHORTAGE  |$922307|$-1000000.00|1.61%     \n",
      "     604|        51|CASH SHORTAGE  |$940196|$-1000000.00|1.54%     \n",
      "     605|        52|CASH SHORTAGE  |$923391|$-1000000.00|1.74%     \n",
      "     606|        52|CASH SHORTAGE  |$923212|$-1000000.00|1.75%     \n",
      "     607|        52|CASH SHORTAGE  |$921999|$-1000000.00|1.86%     \n",
      "     608|        52|CASH SHORTAGE  |$922468|$-1000000.00|1.62%     \n",
      "     609|        52|CASH SHORTAGE  |$922009|$-1000000.00|1.82%     \n",
      "     610|        53|CASH SHORTAGE  |$905738|$-1000000.00|0.19%     \n",
      "     611|        52|CASH SHORTAGE  |$921491|$-1000000.00|0.67%     \n",
      "     612|        52|CASH SHORTAGE  |$921368|$-1000000.00|0.59%     \n",
      "     613|        52|CASH SHORTAGE  |$921696|$-1000000.00|0.63%     \n",
      "     614|        52|CASH SHORTAGE  |$921376|$-1000000.00|0.92%     \n",
      "     615|        51|CASH SHORTAGE  |$940077|$-1000000.00|1.90%     \n",
      "     616|        52|CASH SHORTAGE  |$922094|$-1000000.00|0.52%     \n",
      "     617|        52|CASH SHORTAGE  |$921811|$-1000000.00|0.69%     \n",
      "     618|        51|CASH SHORTAGE  |$941381|$-1000000.00|1.23%     \n",
      "     619|        52|CASH SHORTAGE  |$921364|$-1000000.00|0.47%     \n",
      "     620|        52|CASH SHORTAGE  |$921570|$-1000000.00|0.50%     \n",
      "     621|        51|CASH SHORTAGE  |$940406|$-1000000.00|2.24%     \n",
      "     622|        52|CASH SHORTAGE  |$921688|$-1000000.00|0.18%     \n",
      "     623|        52|CASH SHORTAGE  |$922269|$-1000000.00|0.32%     \n",
      "     624|        52|CASH SHORTAGE  |$921682|$-1000000.00|1.12%     \n",
      "     625|        52|CASH SHORTAGE  |$922595|$-1000000.00|1.43%     \n",
      "     626|        52|CASH SHORTAGE  |$922016|$-1000000.00|0.48%     \n",
      "     627|        53|CASH SHORTAGE  |$905993|$-1000000.00|0.19%     \n",
      "     628|        52|CASH SHORTAGE  |$922638|$-1000000.00|2.27%     \n",
      "     629|        52|CASH SHORTAGE  |$922405|$-1000000.00|1.20%     \n",
      "     630|        52|CASH SHORTAGE  |$922434|$-1000000.00|0.76%     \n",
      "     631|        52|CASH SHORTAGE  |$922335|$-1000000.00|1.95%     \n",
      "     632|        52|CASH SHORTAGE  |$922555|$-1000000.00|1.40%     \n",
      "     633|        52|CASH SHORTAGE  |$921855|$-1000000.00|1.12%     \n",
      "     634|        52|CASH SHORTAGE  |$921948|$-1000000.00|0.94%     \n",
      "     635|        52|CASH SHORTAGE  |$922428|$-1000000.00|1.30%     \n",
      "     636|        52|CASH SHORTAGE  |$922673|$-1000000.00|1.60%     \n",
      "     637|        52|CASH SHORTAGE  |$922667|$-1000000.00|1.89%     \n",
      "     638|        52|CASH SHORTAGE  |$921763|$-1000000.00|1.27%     \n",
      "     639|        52|CASH SHORTAGE  |$922012|$-1000000.00|1.92%     \n",
      "     640|        52|CASH SHORTAGE  |$922444|$-1000000.00|1.33%     \n",
      "     641|        52|CASH SHORTAGE  |$922137|$-1000000.00|1.50%     \n",
      "     642|        52|CASH SHORTAGE  |$923115|$-1000000.00|2.09%     \n",
      "     643|        52|CASH SHORTAGE  |$922324|$-1000000.00|1.50%     \n",
      "     644|        53|CASH SHORTAGE  |$906063|$-1000000.00|1.10%     \n",
      "     645|        53|CASH SHORTAGE  |$906116|$-1000000.00|0.37%     \n",
      "     646|        53|CASH SHORTAGE  |$905671|$-1000000.00|0.09%     \n",
      "     647|        52|CASH SHORTAGE  |$922077|$-1000000.00|0.89%     \n",
      "     648|        52|CASH SHORTAGE  |$922147|$-1000000.00|1.15%     \n",
      "     649|        51|CASH SHORTAGE  |$941027|$-1000000.00|1.93%     \n",
      "     650|        53|CASH SHORTAGE  |$905299|$-1000000.00|0.15%     \n",
      "     651|        52|CASH SHORTAGE  |$921829|$-1000000.00|0.48%     \n",
      "     652|        52|CASH SHORTAGE  |$921313|$-1000000.00|0.57%     \n",
      "     653|        52|CASH SHORTAGE  |$921925|$-1000000.00|0.95%     \n",
      "     654|        52|CASH SHORTAGE  |$922268|$-1000000.00|2.02%     \n",
      "     655|        52|CASH SHORTAGE  |$922517|$-1000000.00|2.27%     \n",
      "     656|        52|CASH SHORTAGE  |$921288|$-1000000.00|1.37%     \n",
      "     657|        52|CASH SHORTAGE  |$922435|$-1000000.00|0.49%     \n",
      "     658|        52|CASH SHORTAGE  |$922879|$-1000000.00|1.91%     \n",
      "     659|        52|CASH SHORTAGE  |$921852|$-1000000.00|0.14%     \n",
      "     660|        52|CASH SHORTAGE  |$921546|$-1000000.00|0.20%     \n",
      "     661|        52|CASH SHORTAGE  |$922610|$-1000000.00|1.45%     \n",
      "     662|        52|CASH SHORTAGE  |$921886|$-1000000.00|1.18%     \n",
      "     663|        52|CASH SHORTAGE  |$920186|$-1000000.00|0.78%     \n",
      "     664|        52|CASH SHORTAGE  |$922080|$-1000000.00|1.01%     \n",
      "     665|        52|CASH SHORTAGE  |$922301|$-1000000.00|0.81%     \n",
      "     666|        52|CASH SHORTAGE  |$922155|$-1000000.00|1.31%     \n",
      "     667|        52|CASH SHORTAGE  |$922056|$-1000000.00|0.40%     \n",
      "     668|        52|CASH SHORTAGE  |$922525|$-1000000.00|1.51%     \n",
      "     669|        52|CASH SHORTAGE  |$921967|$-1000000.00|0.73%     \n",
      "     670|        51|CASH SHORTAGE  |$942034|$-1000000.00|0.62%     \n",
      "     671|        51|CASH SHORTAGE  |$939110|$-1000000.00|2.01%     \n",
      "     672|        51|CASH SHORTAGE  |$941965|$-1000000.00|2.00%     \n",
      "     673|        52|CASH SHORTAGE  |$921223|$-1000000.00|0.80%     \n",
      "     674|        52|CASH SHORTAGE  |$921203|$-1000000.00|1.16%     \n",
      "     675|        52|CASH SHORTAGE  |$922453|$-1000000.00|1.96%     \n",
      "     676|        52|CASH SHORTAGE  |$921048|$-1000000.00|0.40%     \n",
      "     677|        52|CASH SHORTAGE  |$922569|$-1000000.00|1.27%     \n",
      "     678|        52|CASH SHORTAGE  |$921617|$-1000000.00|0.55%     \n",
      "     679|        52|CASH SHORTAGE  |$921296|$-1000000.00|0.49%     \n",
      "     680|        51|CASH SHORTAGE  |$940315|$-1000000.00|1.94%     \n",
      "     681|        51|CASH SHORTAGE  |$939782|$-1000000.00|1.71%     \n",
      "     682|        51|CASH SHORTAGE  |$940397|$-1000000.00|1.78%     \n",
      "     683|        51|CASH SHORTAGE  |$940487|$-1000000.00|1.99%     \n",
      "     684|        51|CASH SHORTAGE  |$941062|$-1000000.00|1.44%     \n",
      "     685|        52|CASH SHORTAGE  |$921074|$-1000000.00|0.89%     \n",
      "     686|        52|CASH SHORTAGE  |$921723|$-1000000.00|0.67%     \n",
      "     687|        51|CASH SHORTAGE  |$940590|$-1000000.00|2.24%     \n",
      "     688|        52|CASH SHORTAGE  |$922219|$-1000000.00|0.98%     \n",
      "     689|        52|CASH SHORTAGE  |$921638|$-1000000.00|0.89%     \n",
      "     690|        51|CASH SHORTAGE  |$940912|$-1000000.00|2.31%     \n",
      "     691|        51|CASH SHORTAGE  |$940550|$-1000000.00|1.63%     \n",
      "     692|        51|CASH SHORTAGE  |$940639|$-1000000.00|1.18%     \n",
      "     693|        51|CASH SHORTAGE  |$940464|$-1000000.00|2.23%     \n",
      "     694|        51|CASH SHORTAGE  |$940094|$-1000000.00|1.83%     \n",
      "     695|        51|CASH SHORTAGE  |$940163|$-1000000.00|1.61%     \n",
      "     696|        52|CASH SHORTAGE  |$921483|$-1000000.00|0.77%     \n",
      "     697|        51|CASH SHORTAGE  |$940370|$-1000000.00|2.10%     \n",
      "     698|        51|CASH SHORTAGE  |$939647|$-1000000.00|1.63%     \n",
      "     699|        51|CASH SHORTAGE  |$939507|$-1000000.00|0.67%     \n",
      "     700|        51|CASH SHORTAGE  |$939829|$-1000000.00|1.40%     \n",
      "     701|        51|CASH SHORTAGE  |$939067|$-1000000.00|2.25%     \n",
      "     702|        51|CASH SHORTAGE  |$939786|$-1000000.00|1.27%     \n",
      "     703|        51|CASH SHORTAGE  |$939469|$-1000000.00|2.10%     \n",
      "     704|        51|CASH SHORTAGE  |$940229|$-1000000.00|1.11%     \n",
      "     705|        51|CASH SHORTAGE  |$940842|$-1000000.00|1.38%     \n",
      "     706|        51|CASH SHORTAGE  |$941067|$-1000000.00|2.34%     \n",
      "     707|        52|CASH SHORTAGE  |$920813|$-1000000.00|0.07%     \n",
      "     708|        50|CASH SHORTAGE  |$924947|$-1000000.00|1.66%     \n",
      "     709|        51|CASH SHORTAGE  |$940334|$-1000000.00|1.05%     \n",
      "     710|        52|CASH SHORTAGE  |$920138|$-1000000.00|0.15%     \n",
      "     711|        51|CASH SHORTAGE  |$940479|$-1000000.00|1.07%     \n",
      "     712|        51|CASH SHORTAGE  |$939984|$-1000000.00|2.31%     \n",
      "     713|        51|CASH SHORTAGE  |$940152|$-1000000.00|1.50%     \n",
      "     714|        51|CASH SHORTAGE  |$940397|$-1000000.00|1.91%     \n",
      "     715|        51|CASH SHORTAGE  |$939810|$-1000000.00|1.40%     \n",
      "     716|        52|CASH SHORTAGE  |$921565|$-1000000.00|0.55%     \n",
      "     717|        51|CASH SHORTAGE  |$940766|$-1000000.00|0.96%     \n",
      "     718|        51|CASH SHORTAGE  |$940699|$-1000000.00|1.11%     \n",
      "     719|        51|CASH SHORTAGE  |$940545|$-1000000.00|2.03%     \n",
      "     720|        51|CASH SHORTAGE  |$940809|$-1000000.00|0.94%     \n",
      "     721|        51|CASH SHORTAGE  |$941334|$-1000000.00|1.60%     \n",
      "     722|        51|CASH SHORTAGE  |$940035|$-1000000.00|1.77%     \n",
      "     723|        51|CASH SHORTAGE  |$941151|$-1000000.00|1.00%     \n",
      "     724|        51|CASH SHORTAGE  |$940592|$-1000000.00|2.32%     \n",
      "     725|        51|CASH SHORTAGE  |$940551|$-1000000.00|2.09%     \n",
      "     726|        52|CASH SHORTAGE  |$920671|$-1000000.00|0.07%     \n",
      "     727|        51|CASH SHORTAGE  |$940652|$-1000000.00|2.21%     \n",
      "     728|        52|CASH SHORTAGE  |$920996|$-1000000.00|0.40%     \n",
      "     729|        52|CASH SHORTAGE  |$920531|$-1000000.00|0.37%     \n",
      "     730|        52|CASH SHORTAGE  |$921416|$-1000000.00|0.39%     \n",
      "     731|        51|CASH SHORTAGE  |$941308|$-1000000.00|1.98%     \n",
      "     732|        52|CASH SHORTAGE  |$921365|$-1000000.00|0.36%     \n",
      "     733|        51|CASH SHORTAGE  |$940460|$-1000000.00|2.56%     \n",
      "     734|        52|CASH SHORTAGE  |$921252|$-1000000.00|0.36%     \n",
      "     735|        52|CASH SHORTAGE  |$921240|$-1000000.00|0.61%     \n",
      "     736|        52|CASH SHORTAGE  |$921015|$-1000000.00|0.59%     \n",
      "     737|        52|CASH SHORTAGE  |$921363|$-1000000.00|0.65%     \n",
      "     738|        52|CASH SHORTAGE  |$921448|$-1000000.00|0.87%     \n",
      "     739|        52|CASH SHORTAGE  |$921063|$-1000000.00|0.79%     \n",
      "     740|        52|CASH SHORTAGE  |$921024|$-1000000.00|0.53%     \n",
      "     741|        51|CASH SHORTAGE  |$940286|$-1000000.00|2.18%     \n",
      "     742|        52|CASH SHORTAGE  |$921058|$-1000000.00|0.22%     \n",
      "     743|        51|CASH SHORTAGE  |$940779|$-1000000.00|2.12%     \n",
      "     744|        52|CASH SHORTAGE  |$920765|$-1000000.00|0.12%     \n",
      "     745|        52|CASH SHORTAGE  |$921227|$-1000000.00|0.14%     \n",
      "     746|        52|CASH SHORTAGE  |$921006|$-1000000.00|0.54%     \n",
      "     747|        52|CASH SHORTAGE  |$921152|$-1000000.00|0.20%     \n",
      "     748|        52|CASH SHORTAGE  |$921279|$-1000000.00|0.09%     \n",
      "     749|        51|CASH SHORTAGE  |$940584|$-1000000.00|1.76%     \n",
      "     750|        52|CASH SHORTAGE  |$920991|$-1000000.00|0.42%     \n",
      "     751|        51|CASH SHORTAGE  |$940641|$-1000000.00|1.86%     \n",
      "     752|        52|CASH SHORTAGE  |$921833|$-1000000.00|0.88%     \n",
      "     753|        52|CASH SHORTAGE  |$921916|$-1000000.00|0.48%     \n",
      "     754|        51|CASH SHORTAGE  |$940616|$-1000000.00|1.95%     \n",
      "     755|        52|CASH SHORTAGE  |$921309|$-1000000.00|0.28%     \n",
      "     756|        52|CASH SHORTAGE  |$921166|$-1000000.00|0.83%     \n",
      "     757|        52|CASH SHORTAGE  |$921276|$-1000000.00|0.22%     \n",
      "     758|        52|CASH SHORTAGE  |$921409|$-1000000.00|0.65%     \n",
      "     759|        51|CASH SHORTAGE  |$940856|$-1000000.00|2.03%     \n",
      "     760|        51|CASH SHORTAGE  |$940377|$-1000000.00|2.13%     \n",
      "     761|        51|CASH SHORTAGE  |$940548|$-1000000.00|2.18%     \n",
      "     762|        52|CASH SHORTAGE  |$921199|$-1000000.00|0.02%     \n",
      "     763|        52|CASH SHORTAGE  |$921089|$-1000000.00|0.20%     \n",
      "     764|        51|CASH SHORTAGE  |$940481|$-1000000.00|2.79%     \n",
      "     765|        52|CASH SHORTAGE  |$921256|$-1000000.00|0.13%     \n",
      "     766|        52|CASH SHORTAGE  |$921367|$-1000000.00|0.58%     \n",
      "     767|        52|CASH SHORTAGE  |$921134|$-1000000.00|0.47%     \n",
      "     768|        52|CASH SHORTAGE  |$921489|$-1000000.00|0.54%     \n",
      "     769|        51|CASH SHORTAGE  |$940318|$-1000000.00|2.51%     \n",
      "     770|        52|CASH SHORTAGE  |$921863|$-1000000.00|0.50%     \n",
      "     771|        51|CASH SHORTAGE  |$941065|$-1000000.00|1.13%     \n",
      "     772|        52|CASH SHORTAGE  |$921255|$-1000000.00|0.06%     \n",
      "     773|        52|CASH SHORTAGE  |$921103|$-1000000.00|0.48%     \n",
      "     774|        52|CASH SHORTAGE  |$921612|$-1000000.00|0.61%     \n",
      "     775|        51|CASH SHORTAGE  |$940685|$-1000000.00|2.16%     \n",
      "     776|        52|CASH SHORTAGE  |$921014|$-1000000.00|0.34%     \n",
      "     777|        52|CASH SHORTAGE  |$921227|$-1000000.00|0.23%     \n",
      "     778|        52|CASH SHORTAGE  |$921082|$-1000000.00|0.62%     \n",
      "     779|        52|CASH SHORTAGE  |$921289|$-1000000.00|0.31%     \n",
      "     780|        52|CASH SHORTAGE  |$921133|$-1000000.00|0.33%     \n",
      "     781|        52|CASH SHORTAGE  |$921340|$-1000000.00|0.71%     \n",
      "     782|        52|CASH SHORTAGE  |$921239|$-1000000.00|0.21%     \n",
      "     783|        51|CASH SHORTAGE  |$940929|$-1000000.00|1.77%     \n",
      "     784|        52|CASH SHORTAGE  |$921109|$-1000000.00|0.40%     \n",
      "     785|        51|CASH SHORTAGE  |$940971|$-1000000.00|2.06%     \n",
      "     786|        51|CASH SHORTAGE  |$940499|$-1000000.00|1.93%     \n",
      "     787|        52|CASH SHORTAGE  |$921750|$-1000000.00|0.01%     \n",
      "     788|        51|CASH SHORTAGE  |$941246|$-1000000.00|1.65%     \n",
      "     789|        52|CASH SHORTAGE  |$921041|$-1000000.00|0.95%     \n",
      "     790|        51|CASH SHORTAGE  |$940417|$-1000000.00|2.23%     \n",
      "     791|        52|CASH SHORTAGE  |$921096|$-1000000.00|0.82%     \n",
      "     792|        52|CASH SHORTAGE  |$921177|$-1000000.00|0.92%     \n",
      "     793|        52|CASH SHORTAGE  |$921170|$-1000000.00|0.59%     \n",
      "     794|        52|CASH SHORTAGE  |$921339|$-1000000.00|0.84%     \n",
      "     795|        52|CASH SHORTAGE  |$921067|$-1000000.00|0.14%     \n",
      "     796|        52|CASH SHORTAGE  |$921187|$-1000000.00|0.43%     \n",
      "     797|        52|CASH SHORTAGE  |$921087|$-1000000.00|0.84%     \n",
      "     798|        52|CASH SHORTAGE  |$921311|$-1000000.00|0.92%     \n",
      "     799|        52|CASH SHORTAGE  |$920938|$-1000000.00|0.43%     \n",
      "     800|        52|CASH SHORTAGE  |$921212|$-1000000.00|0.11%     \n",
      "     801|        52|CASH SHORTAGE  |$921506|$-1000000.00|0.35%     \n",
      "     802|        52|CASH SHORTAGE  |$920572|$-1000000.00|0.49%     \n",
      "     803|        52|CASH SHORTAGE  |$920867|$-1000000.00|0.32%     \n",
      "     804|        52|CASH SHORTAGE  |$921218|$-1000000.00|0.93%     \n",
      "     805|        52|CASH SHORTAGE  |$921039|$-1000000.00|0.79%     \n",
      "     806|        52|CASH SHORTAGE  |$920556|$-1000000.00|0.90%     \n",
      "     807|        52|CASH SHORTAGE  |$920506|$-1000000.00|0.34%     \n",
      "     808|        51|CASH SHORTAGE  |$939844|$-1000000.00|2.13%     \n",
      "     809|        52|CASH SHORTAGE  |$921001|$-1000000.00|0.80%     \n",
      "     810|        52|CASH SHORTAGE  |$920568|$-1000000.00|0.42%     \n",
      "     811|        52|CASH SHORTAGE  |$920223|$-1000000.00|0.47%     \n",
      "     812|        52|CASH SHORTAGE  |$921041|$-1000000.00|0.95%     \n",
      "     813|        52|CASH SHORTAGE  |$921152|$-1000000.00|0.81%     \n",
      "     814|        52|CASH SHORTAGE  |$921352|$-1000000.00|0.19%     \n",
      "     815|        52|CASH SHORTAGE  |$921302|$-1000000.00|0.43%     \n",
      "     816|        51|CASH SHORTAGE  |$939814|$-1000000.00|2.28%     \n",
      "     817|        52|CASH SHORTAGE  |$920399|$-1000000.00|0.44%     \n",
      "     818|        51|CASH SHORTAGE  |$940118|$-1000000.00|1.94%     \n",
      "     819|        51|CASH SHORTAGE  |$940433|$-1000000.00|2.45%     \n",
      "     820|        52|CASH SHORTAGE  |$921388|$-1000000.00|0.14%     \n",
      "     821|        52|CASH SHORTAGE  |$920887|$-1000000.00|0.76%     \n",
      "     822|        52|CASH SHORTAGE  |$920716|$-1000000.00|0.07%     \n"
     ]
    }
   ],
   "source": [
    "model.learn(total_timesteps = 400000, \n",
    "            log_interval = 1, tb_log_name = 'ppo_1024_5_more_ooc_penalty',\n",
    "            reset_num_timesteps = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "j9gZh3HE4ksG",
    "outputId": "5546a860-e99b-4362-ac8d-672936899d65"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([  44.698345 ,  -18.829561 ,   45.69862  ,  100.       ,\n",
      "       -100.       , -100.       ,   75.385124 ,  -95.93753  ,\n",
      "        100.       ,  -34.375946 ,   58.421707 ,   37.250847 ,\n",
      "       -100.       , -100.       ,   31.083208 ,    4.4359975,\n",
      "         42.906803 , -100.       ,  -63.170357 ,  -35.91221  ,\n",
      "        -43.45077  ,   68.828545 ,   28.330017 ,  -54.844772 ,\n",
      "        100.       ,   38.265728 , -100.       ,  -65.30699  ,\n",
      "        100.       ,  100.       ], dtype=float32), array([-100.,  100., -100.,  100.,  100., -100.,  100., -100., -100.,\n",
      "       -100.,  100., -100.,  100., -100., -100.,  100., -100.,  100.,\n",
      "       -100.,  100.,  100., -100., -100., -100.,  100., -100.,  100.,\n",
      "       -100.,  100., -100.], dtype=float32)]\n"
     ]
    }
   ],
   "source": [
    "print(e_train_gym.actions_memory[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "W76k9bze4ksG"
   },
   "outputs": [],
   "source": [
    "model.save(\"quicksave_ppo_dow.model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "efwBi84ch1jE"
   },
   "outputs": [],
   "source": [
    "data_turbulence = processed[(processed.date<'2019-01-01') & (processed.date>='2009-01-01')]\n",
    "insample_turbulence = data_turbulence.drop_duplicates(subset=['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VHZMBpSqh1jG",
    "outputId": "f750f515-9f4f-4adb-846e-ea0bdf15ea6b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2516.000000\n",
       "mean       33.277068\n",
       "std        33.989003\n",
       "min         0.000000\n",
       "25%        15.233736\n",
       "50%        25.180862\n",
       "75%        39.290075\n",
       "max       332.062852\n",
       "Name: turbulence, dtype: float64"
      ]
     },
     "execution_count": 96,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insample_turbulence.turbulence.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "yuwDPkV9h1jL"
   },
   "outputs": [],
   "source": [
    "turbulence_threshold = np.quantile(insample_turbulence.turbulence.values,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wwoz_7VSh1jO",
    "outputId": "37894e93-d22e-4e3f-f23a-d3ca08bf8342"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "332.06285180862903"
      ]
     },
     "execution_count": 98,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "turbulence_threshold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U5mmgQF_h1jQ"
   },
   "source": [
    "### Trade\n",
    "\n",
    "DRL model needs to update periodically in order to take full advantage of the data, ideally we need to retrain our model yearly, quarterly, or monthly. We also need to tune the parameters along the way, in this notebook I only use the in-sample data from 2009-01 to 2018-12 to tune the parameters once, so there is some alpha decay here as the length of trade date extends. \n",
    "\n",
    "Numerous hyperparameters – e.g. the learning rate, the total number of samples to train on – influence the learning process and are usually determined by testing some variations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "eLOnL5eYh1jR",
    "outputId": "cfd8bb67-2fe9-4276-db26-1b6cf52f4e50"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPISODE |STEPS     |TERMINAL_REASON|TOT_ASSETS|TERMINAL_REWARD_unsc|CASH_PCT  \n",
      "       0|       499|update         |$1000789|$-2466.03 |79.20%    \n",
      "       0|       504|Last Date      |$1006240|$6240.31  |79.67%    \n",
      "hit end!\n"
     ]
    }
   ],
   "source": [
    "trade = data_split(processed, '2019-01-01','2021-01-01')\n",
    "e_trade_gym = StockTradingEnv(df = trade,hmax = 10, \n",
    "                              daily_information_cols = information_cols,\n",
    "                              print_verbosity = 500)\n",
    "env_trade, obs_trade = e_trade_gym.get_sb_env()\n",
    "\n",
    "df_account_value, df_actions = DRLAgent.DRL_prediction(model=model,\n",
    "                        test_data = trade,\n",
    "                        test_env = env_trade,\n",
    "                        test_obs = obs_trade)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ERxw3KqLkcP4",
    "outputId": "cbb465c9-38dc-4d88-e79a-6ae29025164b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(504, 5)"
      ]
     },
     "execution_count": 100,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_account_value.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 194
    },
    "id": "2yRkNguY5yvp",
    "outputId": "53ec139f-88e7-4291-cf11-8e6766184265"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cash</th>\n",
       "      <th>asset_value</th>\n",
       "      <th>total_assets</th>\n",
       "      <th>reward</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2019-01-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>992347.843100</td>\n",
       "      <td>7458.537374</td>\n",
       "      <td>9.998064e+05</td>\n",
       "      <td>-193.619526</td>\n",
       "      <td>2019-01-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>990024.253084</td>\n",
       "      <td>10000.944948</td>\n",
       "      <td>1.000025e+06</td>\n",
       "      <td>218.817558</td>\n",
       "      <td>2019-01-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>988329.426153</td>\n",
       "      <td>11743.354637</td>\n",
       "      <td>1.000073e+06</td>\n",
       "      <td>47.582758</td>\n",
       "      <td>2019-01-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>985717.174832</td>\n",
       "      <td>14436.804906</td>\n",
       "      <td>1.000154e+06</td>\n",
       "      <td>81.198948</td>\n",
       "      <td>2019-01-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>980537.121873</td>\n",
       "      <td>19587.117989</td>\n",
       "      <td>1.000124e+06</td>\n",
       "      <td>-29.739876</td>\n",
       "      <td>2019-01-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>975430.357759</td>\n",
       "      <td>24729.901487</td>\n",
       "      <td>1.000160e+06</td>\n",
       "      <td>36.019385</td>\n",
       "      <td>2019-01-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>973839.828134</td>\n",
       "      <td>26275.516742</td>\n",
       "      <td>1.000115e+06</td>\n",
       "      <td>-44.914370</td>\n",
       "      <td>2019-01-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>972701.871908</td>\n",
       "      <td>27289.453522</td>\n",
       "      <td>9.999913e+05</td>\n",
       "      <td>-124.019446</td>\n",
       "      <td>2019-01-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>971880.857121</td>\n",
       "      <td>28202.388424</td>\n",
       "      <td>1.000083e+06</td>\n",
       "      <td>91.920116</td>\n",
       "      <td>2019-01-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>976361.246581</td>\n",
       "      <td>23676.285586</td>\n",
       "      <td>1.000038e+06</td>\n",
       "      <td>-45.713379</td>\n",
       "      <td>2019-01-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>971275.037366</td>\n",
       "      <td>28958.050636</td>\n",
       "      <td>1.000233e+06</td>\n",
       "      <td>195.555835</td>\n",
       "      <td>2019-01-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>964584.393092</td>\n",
       "      <td>36054.682138</td>\n",
       "      <td>1.000639e+06</td>\n",
       "      <td>405.987228</td>\n",
       "      <td>2019-01-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>962998.150749</td>\n",
       "      <td>37346.786565</td>\n",
       "      <td>1.000345e+06</td>\n",
       "      <td>-294.137916</td>\n",
       "      <td>2019-01-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>967766.578231</td>\n",
       "      <td>32699.914024</td>\n",
       "      <td>1.000466e+06</td>\n",
       "      <td>121.554941</td>\n",
       "      <td>2019-01-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>967253.207624</td>\n",
       "      <td>33168.555620</td>\n",
       "      <td>1.000422e+06</td>\n",
       "      <td>-44.729011</td>\n",
       "      <td>2019-01-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>960381.879465</td>\n",
       "      <td>40206.550435</td>\n",
       "      <td>1.000588e+06</td>\n",
       "      <td>166.666656</td>\n",
       "      <td>2019-01-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>964720.770602</td>\n",
       "      <td>35535.677863</td>\n",
       "      <td>1.000256e+06</td>\n",
       "      <td>-331.981435</td>\n",
       "      <td>2019-01-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>961204.640200</td>\n",
       "      <td>39167.216647</td>\n",
       "      <td>1.000372e+06</td>\n",
       "      <td>115.408382</td>\n",
       "      <td>2019-01-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>964428.086963</td>\n",
       "      <td>36330.193306</td>\n",
       "      <td>1.000758e+06</td>\n",
       "      <td>386.423422</td>\n",
       "      <td>2019-01-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>956669.662622</td>\n",
       "      <td>44072.112875</td>\n",
       "      <td>1.000742e+06</td>\n",
       "      <td>-16.504773</td>\n",
       "      <td>2019-01-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>961739.093409</td>\n",
       "      <td>39175.139919</td>\n",
       "      <td>1.000914e+06</td>\n",
       "      <td>172.457831</td>\n",
       "      <td>2019-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>953122.596034</td>\n",
       "      <td>47994.304451</td>\n",
       "      <td>1.001117e+06</td>\n",
       "      <td>202.667158</td>\n",
       "      <td>2019-02-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>958231.716107</td>\n",
       "      <td>43042.930750</td>\n",
       "      <td>1.001275e+06</td>\n",
       "      <td>157.746372</td>\n",
       "      <td>2019-02-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>958238.812767</td>\n",
       "      <td>43008.923374</td>\n",
       "      <td>1.001248e+06</td>\n",
       "      <td>-26.910715</td>\n",
       "      <td>2019-02-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>959457.782831</td>\n",
       "      <td>41488.952969</td>\n",
       "      <td>1.000947e+06</td>\n",
       "      <td>-301.000342</td>\n",
       "      <td>2019-02-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>963606.414545</td>\n",
       "      <td>37327.785655</td>\n",
       "      <td>1.000934e+06</td>\n",
       "      <td>-12.535600</td>\n",
       "      <td>2019-02-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>961541.481521</td>\n",
       "      <td>39403.566098</td>\n",
       "      <td>1.000945e+06</td>\n",
       "      <td>10.847419</td>\n",
       "      <td>2019-02-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>962275.060647</td>\n",
       "      <td>39105.770129</td>\n",
       "      <td>1.001381e+06</td>\n",
       "      <td>435.783158</td>\n",
       "      <td>2019-02-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>962342.550401</td>\n",
       "      <td>39182.941257</td>\n",
       "      <td>1.001525e+06</td>\n",
       "      <td>144.660882</td>\n",
       "      <td>2019-02-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>956038.102112</td>\n",
       "      <td>45262.768962</td>\n",
       "      <td>1.001301e+06</td>\n",
       "      <td>-224.620584</td>\n",
       "      <td>2019-02-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>955839.682608</td>\n",
       "      <td>46076.656562</td>\n",
       "      <td>1.001916e+06</td>\n",
       "      <td>615.468096</td>\n",
       "      <td>2019-02-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>951951.958440</td>\n",
       "      <td>49904.424222</td>\n",
       "      <td>1.001856e+06</td>\n",
       "      <td>-59.956508</td>\n",
       "      <td>2019-02-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>952301.896658</td>\n",
       "      <td>49598.930648</td>\n",
       "      <td>1.001901e+06</td>\n",
       "      <td>44.444644</td>\n",
       "      <td>2019-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>955718.362306</td>\n",
       "      <td>45981.206880</td>\n",
       "      <td>1.001700e+06</td>\n",
       "      <td>-201.258120</td>\n",
       "      <td>2019-02-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>950163.966985</td>\n",
       "      <td>51945.945951</td>\n",
       "      <td>1.002110e+06</td>\n",
       "      <td>410.343750</td>\n",
       "      <td>2019-02-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>953646.045136</td>\n",
       "      <td>48492.050909</td>\n",
       "      <td>1.002138e+06</td>\n",
       "      <td>28.183110</td>\n",
       "      <td>2019-02-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>957764.124018</td>\n",
       "      <td>44397.623509</td>\n",
       "      <td>1.002162e+06</td>\n",
       "      <td>23.651481</td>\n",
       "      <td>2019-02-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>956191.633742</td>\n",
       "      <td>45936.093975</td>\n",
       "      <td>1.002128e+06</td>\n",
       "      <td>-34.019809</td>\n",
       "      <td>2019-02-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>956803.503730</td>\n",
       "      <td>45303.042540</td>\n",
       "      <td>1.002107e+06</td>\n",
       "      <td>-21.181447</td>\n",
       "      <td>2019-02-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>959052.924003</td>\n",
       "      <td>43313.353902</td>\n",
       "      <td>1.002366e+06</td>\n",
       "      <td>259.731636</td>\n",
       "      <td>2019-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>953596.866139</td>\n",
       "      <td>48270.816561</td>\n",
       "      <td>1.001868e+06</td>\n",
       "      <td>-498.595206</td>\n",
       "      <td>2019-03-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>960523.255407</td>\n",
       "      <td>41266.984761</td>\n",
       "      <td>1.001790e+06</td>\n",
       "      <td>-77.442531</td>\n",
       "      <td>2019-03-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>957225.871670</td>\n",
       "      <td>44251.506321</td>\n",
       "      <td>1.001477e+06</td>\n",
       "      <td>-312.862177</td>\n",
       "      <td>2019-03-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>957840.186349</td>\n",
       "      <td>43335.895791</td>\n",
       "      <td>1.001176e+06</td>\n",
       "      <td>-301.295851</td>\n",
       "      <td>2019-03-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>954807.161466</td>\n",
       "      <td>46235.636193</td>\n",
       "      <td>1.001043e+06</td>\n",
       "      <td>-133.284481</td>\n",
       "      <td>2019-03-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>952082.236118</td>\n",
       "      <td>49636.219341</td>\n",
       "      <td>1.001718e+06</td>\n",
       "      <td>675.657801</td>\n",
       "      <td>2019-03-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>954536.368158</td>\n",
       "      <td>47072.528447</td>\n",
       "      <td>1.001609e+06</td>\n",
       "      <td>-109.558854</td>\n",
       "      <td>2019-03-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>944562.813663</td>\n",
       "      <td>57342.666952</td>\n",
       "      <td>1.001905e+06</td>\n",
       "      <td>296.584009</td>\n",
       "      <td>2019-03-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>950753.420030</td>\n",
       "      <td>51024.054920</td>\n",
       "      <td>1.001777e+06</td>\n",
       "      <td>-128.005664</td>\n",
       "      <td>2019-03-14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              cash   asset_value  total_assets      reward        date\n",
       "0   1000000.000000      0.000000  1.000000e+06    0.000000  2019-01-02\n",
       "1    992347.843100   7458.537374  9.998064e+05 -193.619526  2019-01-03\n",
       "2    990024.253084  10000.944948  1.000025e+06  218.817558  2019-01-04\n",
       "3    988329.426153  11743.354637  1.000073e+06   47.582758  2019-01-07\n",
       "4    985717.174832  14436.804906  1.000154e+06   81.198948  2019-01-08\n",
       "5    980537.121873  19587.117989  1.000124e+06  -29.739876  2019-01-09\n",
       "6    975430.357759  24729.901487  1.000160e+06   36.019385  2019-01-10\n",
       "7    973839.828134  26275.516742  1.000115e+06  -44.914370  2019-01-11\n",
       "8    972701.871908  27289.453522  9.999913e+05 -124.019446  2019-01-14\n",
       "9    971880.857121  28202.388424  1.000083e+06   91.920116  2019-01-15\n",
       "10   976361.246581  23676.285586  1.000038e+06  -45.713379  2019-01-16\n",
       "11   971275.037366  28958.050636  1.000233e+06  195.555835  2019-01-17\n",
       "12   964584.393092  36054.682138  1.000639e+06  405.987228  2019-01-18\n",
       "13   962998.150749  37346.786565  1.000345e+06 -294.137916  2019-01-22\n",
       "14   967766.578231  32699.914024  1.000466e+06  121.554941  2019-01-23\n",
       "15   967253.207624  33168.555620  1.000422e+06  -44.729011  2019-01-24\n",
       "16   960381.879465  40206.550435  1.000588e+06  166.666656  2019-01-25\n",
       "17   964720.770602  35535.677863  1.000256e+06 -331.981435  2019-01-28\n",
       "18   961204.640200  39167.216647  1.000372e+06  115.408382  2019-01-29\n",
       "19   964428.086963  36330.193306  1.000758e+06  386.423422  2019-01-30\n",
       "20   956669.662622  44072.112875  1.000742e+06  -16.504773  2019-01-31\n",
       "21   961739.093409  39175.139919  1.000914e+06  172.457831  2019-02-01\n",
       "22   953122.596034  47994.304451  1.001117e+06  202.667158  2019-02-04\n",
       "23   958231.716107  43042.930750  1.001275e+06  157.746372  2019-02-05\n",
       "24   958238.812767  43008.923374  1.001248e+06  -26.910715  2019-02-06\n",
       "25   959457.782831  41488.952969  1.000947e+06 -301.000342  2019-02-07\n",
       "26   963606.414545  37327.785655  1.000934e+06  -12.535600  2019-02-08\n",
       "27   961541.481521  39403.566098  1.000945e+06   10.847419  2019-02-11\n",
       "28   962275.060647  39105.770129  1.001381e+06  435.783158  2019-02-12\n",
       "29   962342.550401  39182.941257  1.001525e+06  144.660882  2019-02-13\n",
       "30   956038.102112  45262.768962  1.001301e+06 -224.620584  2019-02-14\n",
       "31   955839.682608  46076.656562  1.001916e+06  615.468096  2019-02-15\n",
       "32   951951.958440  49904.424222  1.001856e+06  -59.956508  2019-02-19\n",
       "33   952301.896658  49598.930648  1.001901e+06   44.444644  2019-02-20\n",
       "34   955718.362306  45981.206880  1.001700e+06 -201.258120  2019-02-21\n",
       "35   950163.966985  51945.945951  1.002110e+06  410.343750  2019-02-22\n",
       "36   953646.045136  48492.050909  1.002138e+06   28.183110  2019-02-25\n",
       "37   957764.124018  44397.623509  1.002162e+06   23.651481  2019-02-26\n",
       "38   956191.633742  45936.093975  1.002128e+06  -34.019809  2019-02-27\n",
       "39   956803.503730  45303.042540  1.002107e+06  -21.181447  2019-02-28\n",
       "40   959052.924003  43313.353902  1.002366e+06  259.731636  2019-03-01\n",
       "41   953596.866139  48270.816561  1.001868e+06 -498.595206  2019-03-04\n",
       "42   960523.255407  41266.984761  1.001790e+06  -77.442531  2019-03-05\n",
       "43   957225.871670  44251.506321  1.001477e+06 -312.862177  2019-03-06\n",
       "44   957840.186349  43335.895791  1.001176e+06 -301.295851  2019-03-07\n",
       "45   954807.161466  46235.636193  1.001043e+06 -133.284481  2019-03-08\n",
       "46   952082.236118  49636.219341  1.001718e+06  675.657801  2019-03-11\n",
       "47   954536.368158  47072.528447  1.001609e+06 -109.558854  2019-03-12\n",
       "48   944562.813663  57342.666952  1.001905e+06  296.584009  2019-03-13\n",
       "49   950753.420030  51024.054920  1.001777e+06 -128.005664  2019-03-14"
      ]
     },
     "execution_count": 101,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_account_value.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 328
    },
    "id": "nFlK5hNbWVFk",
    "outputId": "06fe8d38-8724-4cea-f6ce-7a2af821ebab"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py:1490: FutureWarning: Using short name for 'orient' is deprecated. Only the options: ('dict', list, 'series', 'split', 'records', 'index') will be used in a future version. Use one of the above to silence this warning.\n",
      "  FutureWarning,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'date': '2019-01-02',\n",
       "  'actions': array([ 10.        ,   7.216348  ,   0.52837026, -10.        ,\n",
       "           0.7429546 ,  -2.905518  ,   3.980421  , -10.        ,\n",
       "         -10.        ,  -5.7391667 , -10.        , -10.        ,\n",
       "          -6.781952  ,  10.        ,   4.536379  ,  10.        ,\n",
       "          10.        , -10.        ,   1.6125104 , -10.        ,\n",
       "          -0.24039909,  -8.1900425 ,  -8.69487   ,  -8.256294  ,\n",
       "          -7.800102  , -10.        ,  10.        ,  -4.7102156 ,\n",
       "           9.329871  ,  -3.8695521 ], dtype=float32)},\n",
       " {'date': '2019-01-03',\n",
       "  'actions': array([-10.        ,  10.        , -10.        ,  -3.622936  ,\n",
       "         -10.        , -10.        , -10.        ,  -9.442478  ,\n",
       "          -3.107138  ,   4.21289   , -10.        ,   1.9327486 ,\n",
       "          -2.990344  , -10.        ,   3.4204676 ,  10.        ,\n",
       "          -5.351473  ,   3.4445453 ,  -0.6890717 , -10.        ,\n",
       "          -9.512228  ,  -0.46926045, -10.        ,  -7.511871  ,\n",
       "          10.        ,  -6.1050935 , -10.        , -10.        ,\n",
       "         -10.        ,   5.580964  ], dtype=float32)},\n",
       " {'date': '2019-01-04',\n",
       "  'actions': array([ -5.8158565 , -10.        , -10.        , -10.        ,\n",
       "          -1.1239755 , -10.        ,   1.3290662 ,   8.749387  ,\n",
       "          -5.154965  , -10.        ,  -0.21236658, -10.        ,\n",
       "          -5.764401  ,   4.9902606 ,  -6.0110054 ,  -0.76103806,\n",
       "         -10.        ,   6.109205  ,   0.9796202 ,  10.        ,\n",
       "           3.1165073 ,  -0.5293992 ,  -7.509753  ,   8.747768  ,\n",
       "          -7.3732686 ,  10.        ,  10.        ,  -1.1630422 ,\n",
       "         -10.        ,   9.598095  ], dtype=float32)}]"
      ]
     },
     "execution_count": 102,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_actions.to_dict(orient = 'rows')[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W6vvNSC6h1jZ"
   },
   "source": [
    "<a id='6'></a>\n",
    "# Part 7: Backtest Our Strategy\n",
    "Backtesting plays a key role in evaluating the performance of a trading strategy. Automated backtesting tool is preferred because it reduces the human error. We usually use the Quantopian pyfolio package to backtest our trading strategies. It is easy to use and consists of various individual plots that provide a comprehensive image of the performance of a trading strategy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Lr2zX7ZxNyFQ"
   },
   "source": [
    "<a id='6.1'></a>\n",
    "## 7.1 BackTestStats\n",
    "pass in df_account_value, this information is stored in env class\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Nzkr9yv-AdV_",
    "outputId": "1053083a-d74c-48b0-a623-de33282e2fff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============Get Backtest Results===========\n",
      "annual return:  0.12883678359865147\n",
      "sharpe ratio:  0.05757882078982788\n",
      "Annual return          0.001038\n",
      "Cumulative returns     0.002074\n",
      "Annual volatility      0.022361\n",
      "Sharpe ratio           0.057579\n",
      "Calmar ratio           0.029074\n",
      "Stability              0.205137\n",
      "Max drawdown          -0.035717\n",
      "Omega ratio            1.011966\n",
      "Sortino ratio          0.078498\n",
      "Skew                  -0.469243\n",
      "Kurtosis               7.562970\n",
      "Tail ratio             0.959550\n",
      "Daily value at risk   -0.002812\n",
      "Alpha                  0.000000\n",
      "Beta                   1.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"==============Get Backtest Results===========\")\n",
    "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
    "\n",
    "perf_stats_all = backtest_stats(account_value=df_account_value, value_col_name = 'total_assets')\n",
    "perf_stats_all = pd.DataFrame(perf_stats_all)\n",
    "perf_stats_all.to_csv(\"./\"+config.RESULTS_DIR+\"/perf_stats_all_\"+now+'.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9U6Suru3h1jc"
   },
   "source": [
    "<a id='6.2'></a>\n",
    "## 7.2 BackTestPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "lKRGftSS7pNM",
    "outputId": "4f77cef2-3934-444a-cacc-4ed8f94514ae"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============Compare to DJIA===========\n",
      "annual return:  0.12883678359865147\n",
      "sharpe ratio:  0.05757882078982788\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (505, 8)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2019-01-03</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2020-12-30</td></tr>\n",
       "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>23</td></tr>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Backtest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Annual return</th>\n",
       "      <td>0.104%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cumulative returns</th>\n",
       "      <td>0.207%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Annual volatility</th>\n",
       "      <td>2.236%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharpe ratio</th>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calmar ratio</th>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stability</th>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max drawdown</th>\n",
       "      <td>-3.572%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Omega ratio</th>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sortino ratio</th>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skew</th>\n",
       "      <td>-0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kurtosis</th>\n",
       "      <td>7.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tail ratio</th>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daily value at risk</th>\n",
       "      <td>-0.281%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alpha</th>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beta</th>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
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       "<IPython.core.display.HTML object>"
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      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Worst drawdown periods</th>\n",
       "      <th>Net drawdown in %</th>\n",
       "      <th>Peak date</th>\n",
       "      <th>Valley date</th>\n",
       "      <th>Recovery date</th>\n",
       "      <th>Duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.57</td>\n",
       "      <td>2019-02-25</td>\n",
       "      <td>2020-03-23</td>\n",
       "      <td>2020-12-02</td>\n",
       "      <td>463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.46</td>\n",
       "      <td>2020-12-08</td>\n",
       "      <td>2020-12-14</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.07</td>\n",
       "      <td>2019-02-05</td>\n",
       "      <td>2019-02-11</td>\n",
       "      <td>2019-02-15</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.06</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>2019-01-29</td>\n",
       "      <td>2019-02-04</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.03</td>\n",
       "      <td>2019-02-20</td>\n",
       "      <td>2019-02-21</td>\n",
       "      <td>2019-02-25</td>\n",
       "      <td>4</td>\n",
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/pyfolio/tears.py:907: UserWarning: Passed returns do not overlap with anyinteresting times.\n",
      "  'interesting times.', UserWarning)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1008x5184 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"==============Compare to DJIA===========\")\n",
    "%matplotlib inline\n",
    "# S&P 500: ^GSPC\n",
    "# Dow Jones Index: ^DJI\n",
    "# NASDAQ 100: ^NDX\n",
    "backtest_plot(df_account_value, \n",
    "             baseline_ticker = '^DJI', \n",
    "             baseline_start = '2019-01-01',\n",
    "             baseline_end = '2021-01-01', value_col_name = 'total_assets')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SlLT9_5WN478"
   },
   "source": [
    "<a id='6.3'></a>\n",
    "## 7.3 Baseline Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YktexHcqh1jc",
    "outputId": "38566531-a3a0-4705-db30-d437e8f8fc73"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============Get Baseline Stats===========\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "Shape of DataFrame:  (505, 8)\n",
      "Annual return          0.144674\n",
      "Cumulative returns     0.310981\n",
      "Annual volatility      0.274619\n",
      "Sharpe ratio           0.631418\n",
      "Calmar ratio           0.390102\n",
      "Stability              0.116677\n",
      "Max drawdown          -0.370862\n",
      "Omega ratio            1.149365\n",
      "Sortino ratio          0.870084\n",
      "Skew                        NaN\n",
      "Kurtosis                    NaN\n",
      "Tail ratio             0.860710\n",
      "Daily value at risk   -0.033911\n",
      "Alpha                  0.000000\n",
      "Beta                   1.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"==============Get Baseline Stats===========\")\n",
    "\n",
    "baseline_df = get_baseline(\n",
    "        ticker=\"^DJI\", \n",
    "        start = '2019-01-01',\n",
    "        end = '2021-01-01')\n",
    "\n",
    "baseline_stats = backtest_stats(baseline_df, value_col_name = 'close')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "A6W2J57ch1j9"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "uijiWgkuh1jB",
    "MRiOtrywfAo1",
    "_gDkU-j-fCmZ",
    "3Zpv4S0-fDBv"
   ],
   "name": "tutorial_multistock_docker.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
